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Contemporary Issues in Communication, Cloud and Big Data Analytics

Proceedings of CCB 2020

• Conference proceedings
• © 2022
• Hiren Kumar Deva Sarma 0 ,
• Valentina Emilia Balas   ORCID: https://orcid.org/0000-0003-0885-1283 1 ,
• Bhaskar Bhuyan 2 ,
• Nitul Dutta 3

Department of Information Technology, Sikkim Manipal Institute of Technology, Majitar, India

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Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

Department of computer science and engineering, marwadi university, rajkot, india.

• Presents research works in the field of communication, cloud and big data
• Provides original works presented at CCB 2020 held in Sikkim, India
• Serves as a reference for researchers and practitioners in academia and industry

Part of the book series: Lecture Notes in Networks and Systems (LNNS, volume 281)

55 Citations

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About this book

This book presents the outcomes of the First International Conference on Communication, Cloud, and Big Data (CCB) held on December 18–19, 2020, at Sikkim Manipal Institute of Technology, Majitar, Sikkim, India. This book contains research papers and articles in the latest topics related to the fields like communication networks, cloud computing, big data analytics, and on various computing techniques. Research papers addressing security issues in above-mentioned areas are also included in the book. The research papers and articles discuss latest issues in the above-mentioned topics. The book is very much helpful and useful for the researchers, engineers, practitioners, research students, and interested readers.

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Network Architectures, Security, and Applications: An Introduction

Introduction and Overview

• Communication Networks
• Cloud Computing

Big Data Analytics

• Network Security
• Cloud Computing Platform
• Big Data Open Platforms

Table of contents (38 papers)

Front matter, communication, reliable data delivery in software-defined networking: a survey.

• Prerna Rai, Hiren Kumar Deva Sarma

Phishing Websites, Detection and Analysis: A Survey

• Leena I. Sakri, Pushpalatha S. Nikkam, Madhuri Kulkarni, Priyanka Kamath, Shreedevi Subrahmanya Bhat, Swati Kamat

Analysis of Security Attacks in SDN Network: A Comprehensive Survey

• Ali Nadim Alhaj, Nitul Dutta

An Overview of 51% Attack Over Bitcoin Network

• Raja Siddharth Raju, Sandeep Gurung, Prativa Rai

An IPS Approach to Secure V-RSU Communication from Blackhole and Wormhole Attacks in VANET

• Gaurav Soni, Kamlesh Chandravanshi, Mahendra Ku. Jhariya, Arjun Rajput

BER Analysis of FBMC for 5G Communication

• Balwant Singh, Malay Ranjan Tripathy, Rishi Asthana

Impact of TCP-SYN Flood Attack in Cloud

• Anurag Sharma, Md. Ruhul Islam, Dhruba Ningombam

An Efficient Cooperative Caching with Request Forwarding Strategy in Information-Centric Networking

• Krishna Delvadia, Nitul Dutta

Instabilities of Consensus

• Priya Ranjan

Delay-Based Approach for Prevention of Rushing Attack in MANETs

• Ashwin Adarsh, Tshering Lhamu Tamang, Payash Pradhan, Vikash Kumar Singh, Biswaraj Sen, Kalpana Sharma

ASCTWNDN:A Simple Caching Tool for Wireless Named Data Networking

• Dependra Dhakal, Mohit Rathor, Sudipta Dey, Prantik Dey, Kalpana Sharma

Design of MIMO Cylindrical DRA’s Using Metalstrip for Enhanced Isolation with Improved Performance

• A. Jayakumar, K. Suresh Kumar, T. Ananth Kumar, S. Sundaresan

A Robust BSP Scheduler for Bioinformatics Application on Public Cloud

• Leena I. Sakri, K. S. Jagadeeshgowda

Mobile Cloud-Based Framework for Health Monitoring with Real-Time Analysis Using Machine Learning Algorithms

• Suman Mohanty, Ravi Anand, Ambarish Dutta, Venktesh Kumar, Utsav Kumar, Md. Ruhul Islam

Genomic Data and Big Data Analytics

• Hiren Kumar Deva Sarma

Image Processing

Editors and affiliations.

Hiren Kumar Deva Sarma, Bhaskar Bhuyan

Valentina Emilia Balas

Nitul Dutta

About the editors

Dr. Hiren Kumar Deva Sarma is Professor in the Department of Information Technology, Sikkim Manipal Institute of Technology, Sikkim. He received Bachelor of Engineering in Mechanical Engineering from Assam Engineering College, Guwahati, Assam (1998). He completed Master of Technology in Information Technology from Tezpur University, Assam (2000). He received Doctor of Philosophy (in Computer Science & Engineering) from Jadavpur University, West Bengal (2013). He has co-authored two books, edited three book volumes, and published more than seventy research papers in different International Journals and referred International and National Conferences of repute. He is the recipient of Young Scientist Award from International Union of Radio Science (URSI) in the XVIII General Assembly 2005, held at New Delhi, India, and has received IEEE Early Adopter Award in 2014. His current research interests are networks, network security, robotics, and big data analytics.  

Dr. Bhaskar Bhuyan is presently working as Associate Professor in the Department of Information Technology, Sikkim Manipal Institute of Technology affiliated to Sikkim Manipal University, Sikkim, India. He did his B.E. (1997) in Computer Science & Engineering from Motilal Nehru Regional Engineering College (now NIT), Allahabad, India.  He did his M.Tech. (2000) in Information Technology and Ph.D. (2017) in Computer Science & Engineering from Tezpur University, Assam, India. He has 18+ years of professional experience in teaching as well as in industry. He has published several research papers in various conferences and journals of repute, and co-edited one book (conference proceedings). His research interests include computer networks, wireless sensor networks, mobile ad hoc networks, Internet of things, and cloud computing.

Bibliographic Information

Book Title : Contemporary Issues in Communication, Cloud and Big Data Analytics

Book Subtitle : Proceedings of CCB 2020

Editors : Hiren Kumar Deva Sarma, Valentina Emilia Balas, Bhaskar Bhuyan, Nitul Dutta

Series Title : Lecture Notes in Networks and Systems

DOI : https://doi.org/10.1007/978-981-16-4244-9

Publisher : Springer Singapore

eBook Packages : Engineering , Engineering (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022

Softcover ISBN : 978-981-16-4243-2 Published: 02 December 2021

eBook ISBN : 978-981-16-4244-9 Published: 30 November 2021

Series ISSN : 2367-3370

Series E-ISSN : 2367-3389

Edition Number : 1

Number of Pages : XVIII, 476

Number of Illustrations : 41 b/w illustrations, 191 illustrations in colour

Topics : Communications Engineering, Networks , Professional Computing , Big Data , Computer Communication Networks

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Study and Investigation on 5G Technology: A Systematic Review

Ramraj dangi.

1 School of Computing Science and Engineering, VIT University Bhopal, Bhopal 466114, India; [email protected] (R.D.); [email protected] (P.L.)

Praveen Lalwani

Gaurav choudhary.

2 Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark; moc.liamg@7777yrahduohcvaruag

3 Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Korea

Giovanni Pau

4 Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy; [email protected]

Associated Data

Not applicable.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

1. Introduction

Most recently, in three decades, rapid growth was marked in the field of wireless communication concerning the transition of 1G to 4G [ 1 , 2 ]. The main motto behind this research was the requirements of high bandwidth and very low latency. 5G provides a high data rate, improved quality of service (QoS), low-latency, high coverage, high reliability, and economically affordable services. 5G delivers services categorized into three categories: (1) Extreme mobile broadband (eMBB). It is a nonstandalone architecture that offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. (2) Massive machine type communication (eMTC), 3GPP releases it in its 13th specification. It provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. (3) ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. [ 3 ].

1.1. Evolution from 1G to 5G

First generation (1G): 1G cell phone was launched between the 1970s and 80s, based on analog technology, which works just like a landline phone. It suffers in various ways, such as poor battery life, voice quality, and dropped calls. In 1G, the maximum achievable speed was 2.4 Kbps.

Second Generation (2G): In 2G, the first digital system was offered in 1991, providing improved mobile voice communication over 1G. In addition, Code-Division Multiple Access (CDMA) and Global System for Mobile (GSM) concepts were also discussed. In 2G, the maximum achievable speed was 1 Mpbs.

Third Generation (3G): When technology ventured from 2G GSM frameworks into 3G universal mobile telecommunication system (UMTS) framework, users encountered higher system speed and quicker download speed making constant video calls. 3G was the first mobile broadband system that was formed to provide the voice with some multimedia. The technology behind 3G was high-speed packet access (HSPA/HSPA+). 3G used MIMO for multiplying the power of the wireless network, and it also used packet switching for fast data transmission.

Fourth Generation (4G): It is purely mobile broadband standard. In digital mobile communication, it was observed information rate that upgraded from 20 to 60 Mbps in 4G [ 4 ]. It works on LTE and WiMAX technologies, as well as provides wider bandwidth up to 100 Mhz. It was launched in 2010.

Fourth Generation LTE-A (4.5G): It is an advanced version of standard 4G LTE. LTE-A uses MIMO technology to combine multiple antennas for both transmitters as well as a receiver. Using MIMO, multiple signals and multiple antennas can work simultaneously, making LTE-A three times faster than standard 4G. LTE-A offered an improved system limit, decreased deferral in the application server, access triple traffic (Data, Voice, and Video) wirelessly at any time anywhere in the world.LTE-A delivers speeds of over 42 Mbps and up to 90 Mbps.

Fifth Generation (5G): 5G is a pillar of digital transformation; it is a real improvement on all the previous mobile generation networks. 5G brings three different services for end user like Extreme mobile broadband (eMBB). It offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. Massive machine type communication (eMTC), it provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. Ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. 5G faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability and scalability, and energy-efficient mobile communication technology [ 6 ]. 5G mainly divided in two parts 6 GHz 5G and Millimeter wave(mmWave) 5G.

6 GHz is a mid frequency band which works as a mid point between capacity and coverage to offer perfect environment for 5G connectivity. 6 GHz spectrum will provide high bandwidth with improved network performance. It offers continuous channels that will reduce the need for network densification when mid-band spectrum is not available and it makes 5G connectivity affordable at anytime, anywhere for everyone.

mmWave is an essential technology of 5G network which build high performance network. 5G mmWave offer diverse services that is why all network providers should add on this technology in their 5G deployment planning. There are lots of service providers who deployed 5G mmWave, and their simulation result shows that 5G mmwave is a far less used spectrum. It provides very high speed wireless communication and it also offers ultra-wide bandwidth for next generation mobile network.

The evolution of wireless mobile technologies are presented in Table 1 . The abbreviations used in this paper are mentioned in Table 2 .

Summary of Mobile Technology.

Table of Notations and Abbreviations.

1.2. Key Contributions

The objective of this survey is to provide a detailed guide of 5G key technologies, methods to researchers, and to help with understanding how the recent works addressed 5G problems and developed solutions to tackle the 5G challenges; i.e., what are new methods that must be applied and how can they solve problems? Highlights of the research article are as follows.

• This survey focused on the recent trends and development in the era of 5G and novel contributions by the researcher community and discussed technical details on essential aspects of the 5G advancement.
• In this paper, the evolution of the mobile network from 1G to 5G is presented. In addition, the growth of mobile communication under different attributes is also discussed.
• This paper covers the emerging applications and research groups working on 5G & different research areas in 5G wireless communication network with a descriptive taxonomy.
• This survey discusses the current vision of the 5G networks, advantages, applications, key technologies, and key features. Furthermore, machine learning prospects are also explored with the emerging requirements in the 5G era. The article also focused on technical aspects of 5G IoT Based approaches and optimization techniques for 5G.
• we provide an extensive overview and recent advancement of emerging technologies of 5G mobile network, namely, MIMO, Non-Orthogonal Multiple Access (NOMA), mmWave, Internet of Things (IoT), Machine Learning (ML), and optimization. Also, a technical summary is discussed by highlighting the context of current approaches and corresponding challenges.
• Security challenges and considerations while developing 5G technology are discussed.
• Finally, the paper concludes with the future directives.

The existing survey focused on architecture, key concepts, and implementation challenges and issues. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products.

2. Existing Surveys and Their Applicability

In this paper, a detailed survey on various technologies of 5G networks is presented. Various researchers have worked on different technologies of 5G networks. In this section, Table 3 gives a tabular representation of existing surveys of 5G networks. Massive MIMO, NOMA, small cell, mmWave, beamforming, and MEC are the six main pillars that helped to implement 5G networks in real life.

A comparative overview of existing surveys on different technologies of 5G networks.

2.1. Limitations of Existing Surveys

The existing survey focused on architecture, key concepts, and implementation challenges and issues. The numerous current surveys focused on various 5G technologies with different parameters, and the authors did not cover all the technologies of the 5G network in detail with challenges and recent advancements. Few authors worked on MIMO (Non-Orthogonal Multiple Access) NOMA, MEC, small cell technologies. In contrast, some others worked on beamforming, Millimeter-wave (mmWave). But the existing survey did not cover all the technologies of the 5G network from a research and advancement perspective. No detailed survey is available in the market covering all the 5G network technologies and currently published research trade-offs. So, our main aim is to give a detailed study of all the technologies working on the 5G network. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products. This survey article collected key information about 5G technology and recent advancements, and it can be a kind of a guide for the reader. This survey provides an umbrella approach to bring multiple solutions and recent improvements in a single place to accelerate the 5G research with the latest key enabling solutions and reviews. A systematic layout representation of the survey in Figure 1 . We provide a state-of-the-art comparative overview of the existing surveys on different technologies of 5G networks in Table 3 .

Systematic layout representation of survey.

2.2. Article Organization

This article is organized under the following sections. Section 2 presents existing surveys and their applicability. In Section 3 , the preliminaries of 5G technology are presented. In Section 4 , recent advances of 5G technology based on Massive MIMO, NOMA, Millimeter Wave, 5G with IoT, machine learning for 5G, and Optimization in 5G are provided. In Section 5 , a description of novel 5G features over 4G is provided. Section 6 covered all the security concerns of the 5G network. Section 7 , 5G technology based on above-stated challenges summarize in tabular form. Finally, Section 8 and Section 9 conclude the study, which paves the path for future research.

3. Preliminary Section

3.1. emerging 5g paradigms and its features.

5G provides very high speed, low latency, and highly salable connectivity between multiple devices and IoT worldwide. 5G will provide a very flexible model to develop a modern generation of applications and industry goals [ 26 , 27 ]. There are many services offered by 5G network architecture are stated below:

Massive machine to machine communications: 5G offers novel, massive machine-to-machine communications [ 28 ], also known as the IoT [ 29 ], that provide connectivity between lots of machines without any involvement of humans. This service enhances the applications of 5G and provides connectivity between agriculture, construction, and industries [ 30 ].

Ultra-reliable low latency communications (URLLC): This service offers real-time management of machines, high-speed vehicle-to-vehicle connectivity, industrial connectivity and security principles, and highly secure transport system, and multiple autonomous actions. Low latency communications also clear up a different area where remote medical care, procedures, and operation are all achievable [ 31 ].

Enhanced mobile broadband: Enhance mobile broadband is an important use case of 5G system, which uses massive MIMO antenna, mmWave, beamforming techniques to offer very high-speed connectivity across a wide range of areas [ 32 ].

For communities: 5G provides a very flexible internet connection between lots of machines to make smart homes, smart schools, smart laboratories, safer and smart automobiles, and good health care centers [ 33 ].

For businesses and industry: As 5G works on higher spectrum ranges from 24 to 100 GHz. This higher frequency range provides secure low latency communication and high-speed wireless connectivity between IoT devices and industry 4.0, which opens a market for end-users to enhance their business models [ 34 ].

New and Emerging technologies: As 5G came up with many new technologies like beamforming, massive MIMO, mmWave, small cell, NOMA, MEC, and network slicing, it introduced many new features to the market. Like virtual reality (VR), users can experience the physical presence of people who are millions of kilometers away from them. Many new technologies like smart homes, smart workplaces, smart schools, smart sports academy also came into the market with this 5G Mobile network model [ 35 ].

3.2. Commercial Service Providers of 5G

5G provides high-speed internet browsing, streaming, and downloading with very high reliability and low latency. 5G network will change your working style, and it will increase new business opportunities and provide innovations that we cannot imagine. This section covers top service providers of 5G network [ 36 , 37 ].

Ericsson: Ericsson is a Swedish multinational networking and telecommunications company, investing around 25.62 billion USD in 5G network, which makes it the biggest telecommunication company. It claims that it is the only company working on all the continents to make the 5G network a global standard for the next generation wireless communication. Ericsson developed the first 5G radio prototype that enables the operators to set up the live field trials in their network, which helps operators understand how 5G reacts. It plays a vital role in the development of 5G hardware. It currently provides 5G services in over 27 countries with content providers like China Mobile, GCI, LGU+, AT&T, Rogers, and many more. It has 100 commercial agreements with different operators as of 2020.

Verizon: It is American multinational telecommunication which was founded in 1983. Verizon started offering 5G services in April 2020, and by December 2020, it has actively provided 5G services in 30 cities of the USA. They planned that by the end of 2021, they would deploy 5G in 30 more new cities. Verizon deployed a 5G network on mmWave, a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave is a faster and high-band spectrum that has a limited range. Verizon planned to increase its number of 5G cells by 500% by 2020. Verizon also has an ultra wide-band flagship 5G service which is the best 5G service that increases the market price of Verizon.

Nokia: Nokia is a Finnish multinational telecommunications company which was founded in 1865. Nokia is one of the companies which adopted 5G technology very early. It is developing, researching, and building partnerships with various 5G renders to offer 5G communication as soon as possible. Nokia collaborated with Deutsche Telekom and Hamburg Port Authority and provided them 8000-hectare site for their 5G MoNArch project. Nokia is the only company that supplies 5G technology to all the operators of different countries like AT&T, Sprint, T-Mobile US and Verizon in the USA, Korea Telecom, LG U+ and SK Telecom in South Korea and NTT DOCOMO, KDDI, and SoftBank in Japan. Presently, Nokia has around 150+ agreements and 29 live networks all over the world. Nokia is continuously working hard on 5G technology to expand 5G networks all over the globe.

AT&T: AT&T is an American multinational company that was the first to deploy a 5G network in reality in 2018. They built a gigabit 5G network connection in Waco, TX, Kalamazoo, MI, and South Bend to achieve this. It is the first company that archives 1–2 gigabit per second speed in 2019. AT&T claims that it provides a 5G network connection among 225 million people worldwide by using a 6 GHz spectrum band.

T-Mobile: T-Mobile US (TMUS) is an American wireless network operator which was the first service provider that offers a real 5G nationwide network. The company knew that high-band 5G was not feasible nationwide, so they used a 600 MHz spectrum to build a significant portion of its 5G network. TMUS is planning that by 2024 they will double the total capacity and triple the full 5G capacity of T-Mobile and Sprint combined. The sprint buyout is helping T-Mobile move forward the company’s current market price to 129.98 USD.

Samsung: Samsung started their research in 5G technology in 2011. In 2013, Samsung successfully developed the world’s first adaptive array transceiver technology operating in the millimeter-wave Ka bands for cellular communications. Samsung provides several hundred times faster data transmission than standard 4G for core 5G mobile communication systems. The company achieved a lot of success in the next generation of technology, and it is considered one of the leading companies in the 5G domain.

Qualcomm: Qualcomm is an American multinational corporation in San Diego, California. It is also one of the leading company which is working on 5G chip. Qualcomm’s first 5G modem chip was announced in October 2016, and a prototype was demonstrated in October 2017. Qualcomm mainly focuses on building products while other companies talk about 5G; Qualcomm is building the technologies. According to one magazine, Qualcomm was working on three main areas of 5G networks. Firstly, radios that would use bandwidth from any network it has access to; secondly, creating more extensive ranges of spectrum by combining smaller pieces; and thirdly, a set of services for internet applications.

ZTE Corporation: ZTE Corporation was founded in 1985. It is a partially Chinese state-owned technology company that works in telecommunication. It was a leading company that worked on 4G LTE, and it is still maintaining its value and doing research and tests on 5G. It is the first company that proposed Pre5G technology with some series of solutions.

NEC Corporation: NEC Corporation is a Japanese multinational information technology and electronics corporation headquartered in Minato, Tokyo. ZTE also started their research on 5G, and they introduced a new business concept. NEC’s main aim is to develop 5G NR for the global mobile system and create secure and intelligent technologies to realize 5G services.

Cisco: Cisco is a USA networking hardware company that also sleeves up for 5G network. Cisco’s primary focus is to support 5G in three ways: Service—enable 5G services faster so all service providers can increase their business. Infrastructure—build 5G-oriented infrastructure to implement 5G more quickly. Automation—make a more scalable, flexible, and reliable 5G network. The companies know the importance of 5G, and they want to connect more than 30 billion devices in the next couple of years. Cisco intends to work on network hardening as it is a vital part of 5G network. Cisco used AI with deep learning to develop a 5G Security Architecture, enabling Secure Network Transformation.

3.3. 5G Research Groups

Many research groups from all over the world are working on a 5G wireless mobile network [ 38 ]. These groups are continuously working on various aspects of 5G. The list of those research groups are presented as follows: 5GNOW (5th Generation Non-Orthogonal Waveform for Asynchronous Signaling), NEWCOM (Network of Excellence in Wireless Communication), 5GIC (5G Innovation Center), NYU (New York University) Wireless, 5GPPP (5G Infrastructure Public-Private Partnership), EMPHATIC (Enhanced Multi-carrier Technology for Professional Adhoc and Cell-Based Communication), ETRI(Electronics and Telecommunication Research Institute), METIS (Mobile and wireless communication Enablers for the Twenty-twenty Information Society) [ 39 ]. The various research groups along with the research area are presented in Table 4 .

Research groups working on 5G mobile networks.

3.4. 5G Applications

5G is faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability, greater scalablility, and energy-efficient mobile communication technology [ 6 ].

There are lots of applications of 5G mobile network are as follows:

• High-speed mobile network: 5G is an advancement on all the previous mobile network technologies, which offers very high speed downloading speeds 0 of up to 10 to 20 Gbps. The 5G wireless network works as a fiber optic internet connection. 5G is different from all the conventional mobile transmission technologies, and it offers both voice and high-speed data connectivity efficiently. 5G offers very low latency communication of less than a millisecond, useful for autonomous driving and mission-critical applications. 5G will use millimeter waves for data transmission, providing higher bandwidth and a massive data rate than lower LTE bands. As 5 Gis a fast mobile network technology, it will enable virtual access to high processing power and secure and safe access to cloud services and enterprise applications. Small cell is one of the best features of 5G, which brings lots of advantages like high coverage, high-speed data transfer, power saving, easy and fast cloud access, etc. [ 40 ].
• Entertainment and multimedia: In one analysis in 2015, it was found that more than 50 percent of mobile internet traffic was used for video downloading. This trend will surely increase in the future, which will make video streaming more common. 5G will offer High-speed streaming of 4K videos with crystal clear audio, and it will make a high definition virtual world on your mobile. 5G will benefit the entertainment industry as it offers 120 frames per second with high resolution and higher dynamic range video streaming, and HD TV channels can also be accessed on mobile devices without any interruptions. 5G provides low latency high definition communication so augmented reality (AR), and virtual reality (VR) will be very easily implemented in the future. Virtual reality games are trendy these days, and many companies are investing in HD virtual reality games. The 5G network will offer high-speed internet connectivity with a better gaming experience [ 41 ].
• Smart homes : smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high-speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network as it offers very high-speed low latency communication.
• Smart cities: 5G wireless network also helps develop smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy-saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.
• Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance, and logistics. 5G smart sensor technology also offers smarter, safer, cost-effective, and energy-saving industrial IoT operations.
• Smart Farming: 5G technology will play a crucial role in agriculture and smart farming. 5G sensors and GPS technology will help farmers track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation, pest, insect, and electricity control.
• Autonomous Driving: The 5G wireless network offers very low latency high-speed communication, significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects, and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is essential for autonomous vehicles, decision-making is done in microseconds to avoid accidents.
• Healthcare and mission-critical applications: 5G technology will bring modernization in medicine where doctors and practitioners can perform advanced medical procedures. The 5G network will provide connectivity between all classrooms, so attending seminars and lectures will be easier. Through 5G technology, patients can connect with doctors and take their advice. Scientists are building smart medical devices which can help people with chronic medical conditions. The 5G network will boost the healthcare industry with smart devices, the internet of medical things, smart sensors, HD medical imaging technologies, and smart analytics systems. 5G will help access cloud storage, so accessing healthcare data will be very easy from any location worldwide. Doctors and medical practitioners can easily store and share large files like MRI reports within seconds using the 5G network.
• Satellite Internet: In many remote areas, ground base stations are not available, so 5G will play a crucial role in providing connectivity in such areas. The 5G network will provide connectivity using satellite systems, and the satellite system uses a constellation of multiple small satellites to provide connectivity in urban and rural areas across the world.

4. 5G Technologies

This section describes recent advances of 5G Massive MIMO, 5G NOMA, 5G millimeter wave, 5G IOT, 5G with machine learning, and 5G optimization-based approaches. In addition, the summary is also presented in each subsection that paves the researchers for the future research direction.

4.1. 5G Massive MIMO

Multiple-input-multiple-out (MIMO) is a very important technology for wireless systems. It is used for sending and receiving multiple signals simultaneously over the same radio channel. MIMO plays a very big role in WI-FI, 3G, 4G, and 4G LTE-A networks. MIMO is mainly used to achieve high spectral efficiency and energy efficiency but it was not up to the mark MIMO provides low throughput and very low reliable connectivity. To resolve this, lots of MIMO technology like single user MIMO (SU-MIMO), multiuser MIMO (MU-MIMO) and network MIMO were used. However, these new MIMO also did not still fulfill the demand of end users. Massive MIMO is an advancement of MIMO technology used in the 5G network in which hundreds and thousands of antennas are attached with base stations to increase throughput and spectral efficiency. Multiple transmit and receive antennas are used in massive MIMO to increase the transmission rate and spectral efficiency. When multiple UEs generate downlink traffic simultaneously, massive MIMO gains higher capacity. Massive MIMO uses extra antennas to move energy into smaller regions of space to increase spectral efficiency and throughput [ 43 ]. In traditional systems data collection from smart sensors is a complex task as it increases latency, reduced data rate and reduced reliability. While massive MIMO with beamforming and huge multiplexing techniques can sense data from different sensors with low latency, high data rate and higher reliability. Massive MIMO will help in transmitting the data in real-time collected from different sensors to central monitoring locations for smart sensor applications like self-driving cars, healthcare centers, smart grids, smart cities, smart highways, smart homes, and smart enterprises [ 44 ].

Highlights of 5G Massive MIMO technology are as follows:

• Data rate: Massive MIMO is advised as the one of the dominant technologies to provide wireless high speed and high data rate in the gigabits per seconds.
• The relationship between wave frequency and antenna size: Both are inversely proportional to each other. It means lower frequency signals need a bigger antenna and vise versa.

Pictorial representation of multi-input and multi-output (MIMO).

• MIMO role in 5G: Massive MIMO will play a crucial role in the deployment of future 5G mobile communication as greater spectral and energy efficiency could be enabled.

State-of-the-Art Approaches

Plenty of approaches were proposed to resolve the issues of conventional MIMO [ 7 ].

The MIMO multirate, feed-forward controller is suggested by Mae et al. [ 46 ]. In the simulation, the proposed model generates the smooth control input, unlike the conventional MIMO, which generates oscillated control inputs. It also outperformed concerning the error rate. However, a combination of multirate and single rate can be used for better results.

The performance of stand-alone MIMO, distributed MIMO with and without corporation MIMO, was investigated by Panzner et al. [ 47 ]. In addition, an idea about the integration of large scale in the 5G technology was also presented. In the experimental analysis, different MIMO configurations are considered. The variation in the ratio of overall transmit antennas to spatial is deemed step-wise from equality to ten.

The simulation of massive MIMO noncooperative and cooperative systems for down-link behavior was performed by He et al. [ 48 ]. It depends on present LTE systems, which deal with various antennas in the base station set-up. It was observed that collaboration in different BS improves the system behaviors, whereas throughput is reduced slightly in this approach. However, a new method can be developed which can enhance both system behavior and throughput.

In [ 8 ], different approaches that increased the energy efficiency benefits provided by massive MIMO were presented. They analyzed the massive MIMO technology and described the detailed design of the energy consumption model for massive MIMO systems. This article has explored several techniques to enhance massive MIMO systems’ energy efficiency (EE) gains. This paper reviews standard EE-maximization approaches for the conventional massive MIMO systems, namely, scaling number of antennas, real-time implementing low-complexity operations at the base station (BS), power amplifier losses minimization, and radio frequency (RF) chain minimization requirements. In addition, open research direction is also identified.

In [ 49 ], various existing approaches based on different antenna selection and scheduling, user selection and scheduling, and joint antenna and user scheduling methods adopted in massive MIMO systems are presented in this paper. The objective of this survey article was to make awareness about the current research and future research direction in MIMO for systems. They analyzed that complete utilization of resources and bandwidth was the most crucial factor which enhances the sum rate.

In [ 50 ], authors discussed the development of various techniques for pilot contamination. To calculate the impact of pilot contamination in time division duplex (TDD) massive MIMO system, TDD and frequency division duplexing FDD patterns in massive MIMO techniques are used. They discussed different issues in pilot contamination in TDD massive MIMO systems with all the possible future directions of research. They also classified various techniques to generate the channel information for both pilot-based and subspace-based approaches.

In [ 19 ], the authors defined the uplink and downlink services for a massive MIMO system. In addition, it maintains a performance matrix that measures the impact of pilot contamination on different performances. They also examined the various application of massive MIMO such as small cells, orthogonal frequency-division multiplexing (OFDM) schemes, massive MIMO IEEE 802, 3rd generation partnership project (3GPP) specifications, and higher frequency bands. They considered their research work crucial for cutting edge massive MIMO and covered many issues like system throughput performance and channel state acquisition at higher frequencies.

In [ 13 ], various approaches were suggested for MIMO future generation wireless communication. They made a comparative study based on performance indicators such as peak data rate, energy efficiency, latency, throughput, etc. The key findings of this survey are as follows: (1) spatial multiplexing improves the energy efficiency; (2) design of MIMO play a vital role in the enhancement of throughput; (3) enhancement of mMIMO focusing on energy & spectral performance; (4) discussed the future challenges to improve the system design.

In [ 51 ], the study of large-scale MIMO systems for an energy-efficient system sharing method was presented. For the resource allocation, circuit energy and transmit energy expenditures were taken into consideration. In addition, the optimization techniques were applied for an energy-efficient resource sharing system to enlarge the energy efficiency for individual QoS and energy constraints. The author also examined the BS configuration, which includes homogeneous and heterogeneous UEs. While simulating, they discussed that the total number of transmit antennas plays a vital role in boosting energy efficiency. They highlighted that the highest energy efficiency was obtained when the BS was set up with 100 antennas that serve 20 UEs.

This section includes various works done on 5G MIMO technology by different author’s. Table 5 shows how different author’s worked on improvement of various parameters such as throughput, latency, energy efficiency, and spectral efficiency with 5G MIMO technology.

Summary of massive MIMO-based approaches in 5G technology.

4.2. 5G Non-Orthogonal Multiple Access (NOMA)

NOMA is a very important radio access technology used in next generation wireless communication. Compared to previous orthogonal multiple access techniques, NOMA offers lots of benefits like high spectrum efficiency, low latency with high reliability and high speed massive connectivity. NOMA mainly works on a baseline to serve multiple users with the same resources in terms of time, space and frequency. NOMA is mainly divided into two main categories one is code domain NOMA and another is power domain NOMA. Code-domain NOMA can improve the spectral efficiency of mMIMO, which improves the connectivity in 5G wireless communication. Code-domain NOMA was divided into some more multiple access techniques like sparse code multiple access, lattice-partition multiple access, multi-user shared access and pattern-division multiple access [ 52 ]. Power-domain NOMA is widely used in 5G wireless networks as it performs well with various wireless communication techniques such as MIMO, beamforming, space-time coding, network coding, full-duplex and cooperative communication etc. [ 53 ]. The conventional orthogonal frequency-division multiple access (OFDMA) used by 3GPP in 4G LTE network provides very low spectral efficiency when bandwidth resources are allocated to users with low channel state information (CSI). NOMA resolved this issue as it enables users to access all the subcarrier channels so bandwidth resources allocated to the users with low CSI can still be accessed by the users with strong CSI which increases the spectral efficiency. The 5G network will support heterogeneous architecture in which small cell and macro base stations work for spectrum sharing. NOMA is a key technology of the 5G wireless system which is very helpful for heterogeneous networks as multiple users can share their data in a small cell using the NOMA principle.The NOMA is helpful in various applications like ultra-dense networks (UDN), machine to machine (M2M) communication and massive machine type communication (mMTC). As NOMA provides lots of features it has some challenges too such as NOMA needs huge computational power for a large number of users at high data rates to run the SIC algorithms. Second, when users are moving from the networks, to manage power allocation optimization is a challenging task for NOMA [ 54 ]. Hybrid NOMA (HNOMA) is a combination of power-domain and code-domain NOMA. HNOMA uses both power differences and orthogonal resources for transmission among multiple users. As HNOMA is using both power-domain NOMA and code-domain NOMA it can achieve higher spectral efficiency than Power-domain NOMA and code-domain NOMA. In HNOMA multiple groups can simultaneously transmit signals at the same time. It uses a message passing algorithm (MPA) and successive interference cancellation (SIC)-based detection at the base station for these groups [ 55 ].

Highlights of 5G NOMA technology as follows:

Pictorial representation of orthogonal and Non-Orthogonal Multiple Access (NOMA).

• NOMA provides higher data rates and resolves all the loop holes of OMA that makes 5G mobile network more scalable and reliable.
• As multiple users use same frequency band simultaneously it increases the performance of whole network.
• To setup intracell and intercell interference NOMA provides nonorthogonal transmission on the transmitter end.
• The primary fundamental of NOMA is to improve the spectrum efficiency by strengthening the ramification of receiver.

State-of-the-Art of Approaches

A plenty of approaches were developed to address the various issues in NOMA.

A novel approach to address the multiple receiving signals at the same frequency is proposed in [ 22 ]. In NOMA, multiple users use the same sub-carrier, which improves the fairness and throughput of the system. As a nonorthogonal method is used among multiple users, at the time of retrieving the user’s signal at the receiver’s end, joint processing is required. They proposed solutions to optimize the receiver and the radio resource allocation of uplink NOMA. Firstly, the authors proposed an iterative MUDD which utilizes the information produced by the channel decoder to improve the performance of the multiuser detector. After that, the author suggested a power allocation and novel subcarrier that enhances the users’ weighted sum rate for the NOMA scheme. Their proposed model showed that NOMA performed well as compared to OFDM in terms of fairness and efficiency.

In [ 53 ], the author’s reviewed a power-domain NOMA that uses superposition coding (SC) and successive interference cancellation (SIC) at the transmitter and the receiver end. Lots of analyses were held that described that NOMA effectively satisfies user data rate demands and network-level of 5G technologies. The paper presented a complete review of recent advances in the 5G NOMA system. It showed the comparative analysis regarding allocation procedures, user fairness, state-of-the-art efficiency evaluation, user pairing pattern, etc. The study also analyzes NOMA’s behavior when working with other wireless communication techniques, namely, beamforming, MIMO, cooperative connections, network, space-time coding, etc.

In [ 9 ], the authors proposed NOMA with MEC, which improves the QoS as well as reduces the latency of the 5G wireless network. This model increases the uplink NOMA by decreasing the user’s uplink energy consumption. They formulated an optimized NOMA framework that reduces the energy consumption of MEC by using computing and communication resource allocation, user clustering, and transmit powers.

In [ 10 ], the authors proposed a model which investigates outage probability under average channel state information CSI and data rate in full CSI to resolve the problem of optimal power allocation, which increase the NOMA downlink system among users. They developed simple low-complexity algorithms to provide the optimal solution. The obtained simulation results showed NOMA’s efficiency, achieving higher performance fairness compared to the TDMA configurations. It was observed from the results that NOMA, through the appropriate power amplifiers (PA), ensures the high-performance fairness requirement for the future 5G wireless communication networks.

In [ 56 ], researchers discussed that the NOMA technology and waveform modulation techniques had been used in the 5G mobile network. Therefore, this research gave a detailed survey of non-orthogonal waveform modulation techniques and NOMA schemes for next-generation mobile networks. By analyzing and comparing multiple access technologies, they considered the future evolution of these technologies for 5G mobile communication.

In [ 57 ], the authors surveyed non-orthogonal multiple access (NOMA) from the development phase to the recent developments. They have also compared NOMA techniques with traditional OMA techniques concerning information theory. The author discussed the NOMA schemes categorically as power and code domain, including the design principles, operating principles, and features. Comparison is based upon the system’s performance, spectral efficiency, and the receiver’s complexity. Also discussed are the future challenges, open issues, and their expectations of NOMA and how it will support the key requirements of 5G mobile communication systems with massive connectivity and low latency.

In [ 17 ], authors present the first review of an elementary NOMA model with two users, which clarify its central precepts. After that, a general design with multicarrier supports with a random number of users on each sub-carrier is analyzed. In performance evaluation with the existing approaches, resource sharing and multiple-input multiple-output NOMA are examined. Furthermore, they took the key elements of NOMA and its potential research demands. Finally, they reviewed the two-user SC-NOMA design and a multi-user MC-NOMA design to highlight NOMA’s basic approaches and conventions. They also present the research study about the performance examination, resource assignment, and MIMO in NOMA.

In this section, various works by different authors done on 5G NOMA technology is covered. Table 6 shows how other authors worked on the improvement of various parameters such as spectral efficiency, fairness, and computing capacity with 5G NOMA technology.

Summary of NOMA-based approaches in 5G technology.

4.3. 5G Millimeter Wave (mmWave)

Millimeter wave is an extremely high frequency band, which is very useful for 5G wireless networks. MmWave uses 30 GHz to 300 GHz spectrum band for transmission. The frequency band between 30 GHz to 300 GHz is known as mmWave because these waves have wavelengths between 1 to 10 mm. Till now radar systems and satellites are only using mmWave as these are very fast frequency bands which provide very high speed wireless communication. Many mobile network providers also started mmWave for transmitting data between base stations. Using two ways the speed of data transmission can be improved one is by increasing spectrum utilization and second is by increasing spectrum bandwidth. Out of these two approaches increasing bandwidth is quite easy and better. The frequency band below 5 GHz is very crowded as many technologies are using it so to boost up the data transmission rate 5G wireless network uses mmWave technology which instead of increasing spectrum utilization, increases the spectrum bandwidth [ 58 ]. To maximize the signal bandwidth in wireless communication the carrier frequency should also be increased by 5% because the signal bandwidth is directly proportional to carrier frequencies. The frequency band between 28 GHz to 60 GHz is very useful for 5G wireless communication as 28 GHz frequency band offers up to 1 GHz spectrum bandwidth and 60 GHz frequency band offers 2 GHz spectrum bandwidth. 4G LTE provides 2 GHz carrier frequency which offers only 100 MHz spectrum bandwidth. However, the use of mmWave increases the spectrum bandwidth 10 times, which leads to better transmission speeds [ 59 , 60 ].

Highlights of 5G mmWave are as follows:

Pictorial representation of millimeter wave.

• The 5G mmWave offer three advantages: (1) MmWave is very less used new Band, (2) MmWave signals carry more data than lower frequency wave, and (3) MmWave can be incorporated with MIMO antenna with the potential to offer a higher magnitude capacity compared to current communication systems.

In [ 11 ], the authors presented the survey of mmWave communications for 5G. The advantage of mmWave communications is adaptability, i.e., it supports the architectures and protocols up-gradation, which consists of integrated circuits, systems, etc. The authors over-viewed the present solutions and examined them concerning effectiveness, performance, and complexity. They also discussed the open research issues of mmWave communications in 5G concerning the software-defined network (SDN) architecture, network state information, efficient regulation techniques, and the heterogeneous system.

In [ 61 ], the authors present the recent work done by investigators in 5G; they discussed the design issues and demands of mmWave 5G antennas for cellular handsets. After that, they designed a small size and low-profile 60 GHz array of antenna units that contain 3D planer mesh-grid antenna elements. For the future prospect, a framework is designed in which antenna components are used to operate cellular handsets on mmWave 5G smartphones. In addition, they cross-checked the mesh-grid array of antennas with the polarized beam for upcoming hardware challenges.

In [ 12 ], the authors considered the suitability of the mmWave band for 5G cellular systems. They suggested a resource allocation system for concurrent D2D communications in mmWave 5G cellular systems, and it improves network efficiency and maintains network connectivity. This research article can serve as guidance for simulating D2D communications in mmWave 5G cellular systems. Massive mmWave BS may be set up to obtain a high delivery rate and aggregate efficiency. Therefore, many wireless users can hand off frequently between the mmWave base terminals, and it emerges the demand to search the neighbor having better network connectivity.

In [ 62 ], the authors provided a brief description of the cellular spectrum which ranges from 1 GHz to 3 GHz and is very crowed. In addition, they presented various noteworthy factors to set up mmWave communications in 5G, namely, channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous, and rain. In addition, hybrid beamforming architecture in the mmWave technique is analyzed. They also suggested methods for the blockage effect in mmWave communications due to penetration damage. Finally, the authors have studied designing the mmWave transmission with small beams in nonorthogonal device-to-device communication.

This section covered various works done on 5G mmWave technology. The Table 7 shows how different author’s worked on the improvement of various parameters i.e., transmission rate, coverage, and cost, with 5G mmWave technology.

Summary of existing mmWave-based approaches in 5G technology.

4.4. 5G IoT Based Approaches

The 5G mobile network plays a big role in developing the Internet of Things (IoT). IoT will connect lots of things with the internet like appliances, sensors, devices, objects, and applications. These applications will collect lots of data from different devices and sensors. 5G will provide very high speed internet connectivity for data collection, transmission, control, and processing. 5G is a flexible network with unused spectrum availability and it offers very low cost deployment that is why it is the most efficient technology for IoT [ 63 ]. In many areas, 5G provides benefits to IoT, and below are some examples:

Smart homes: smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network, as it offers very high speed low latency communication.

Smart cities: 5G wireless network also helps in developing smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.

Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance and logistics. 5G smart sensor technology also offers smarter, safer, cost effective, and energy-saving industrial operation for industrial IoT.

Smart Farming: 5G technology will play a crucial role for agriculture and smart farming. 5G sensors and GPS technology will help farmers to track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation control, pest control, insect control, and electricity control.

Autonomous Driving: 5G wireless network offers very low latency high speed communication which is very significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is important for autonomous vehicles, decision taking is performed in microseconds to avoid accidents [ 64 ].

Highlights of 5G IoT are as follows:

Pictorial representation of IoT with 5G.

• 5G with IoT is a new feature of next-generation mobile communication, which provides a high-speed internet connection between moderated devices. 5G IoT also offers smart homes, smart devices, sensors, smart transportation systems, smart industries, etc., for end-users to make them smarter.
• IoT deals with moderate devices which connect through the internet. The approach of the IoT has made the consideration of the research associated with the outcome of providing wearable, smart-phones, sensors, smart transportation systems, smart devices, washing machines, tablets, etc., and these diverse systems are associated to a common interface with the intelligence to connect.
• Significant IoT applications include private healthcare systems, traffic management, industrial management, and tactile internet, etc.

Plenty of approaches is devised to address the issues of IoT [ 14 , 65 , 66 ].

In [ 65 ], the paper focuses on 5G mobile systems due to the emerging trends and developing technologies, which results in the exponential traffic growth in IoT. The author surveyed the challenges and demands during deployment of the massive IoT applications with the main focus on mobile networking. The author reviewed the features of standard IoT infrastructure, along with the cellular-based, low-power wide-area technologies (LPWA) such as eMTC, extended coverage (EC)-GSM-IoT, as well as noncellular, low-power wide-area (LPWA) technologies such as SigFox, LoRa etc.

In [ 14 ], the authors presented how 5G technology copes with the various issues of IoT today. It provides a brief review of existing and forming 5G architectures. The survey indicates the role of 5G in the foundation of the IoT ecosystem. IoT and 5G can easily combine with improved wireless technologies to set up the same ecosystem that can fulfill the current requirement for IoT devices. 5G can alter nature and will help to expand the development of IoT devices. As the process of 5G unfolds, global associations will find essentials for setting up a cross-industry engagement in determining and enlarging the 5G system.

In [ 66 ], the author introduced an IoT authentication scheme in a 5G network, with more excellent reliability and dynamic. The scheme proposed a privacy-protected procedure for selecting slices; it provided an additional fog node for proper data transmission and service types of the subscribers, along with service-oriented authentication and key understanding to maintain the secrecy, precision of users, and confidentiality of service factors. Users anonymously identify the IoT servers and develop a vital channel for service accessibility and data cached on local fog nodes and remote IoT servers. The author performed a simulation to manifest the security and privacy preservation of the user over the network.

This section covered various works done on 5G IoT by multiple authors. Table 8 shows how different author’s worked on the improvement of numerous parameters, i.e., data rate, security requirement, and performance with 5G IoT.

Summary of IoT-based approaches in 5G technology.

4.5. Machine Learning Techniques for 5G

Various machine learning (ML) techniques were applied in 5G networks and mobile communication. It provides a solution to multiple complex problems, which requires a lot of hand-tuning. ML techniques can be broadly classified as supervised, unsupervised, and reinforcement learning. Let’s discuss each learning technique separately and where it impacts the 5G network.

Supervised Learning, where user works with labeled data; some 5G network problems can be further categorized as classification and regression problems. Some regression problems such as scheduling nodes in 5G and energy availability can be predicted using Linear Regression (LR) algorithm. To accurately predict the bandwidth and frequency allocation Statistical Logistic Regression (SLR) is applied. Some supervised classifiers are applied to predict the network demand and allocate network resources based on the connectivity performance; it signifies the topology setup and bit rates. Support Vector Machine (SVM) and NN-based approximation algorithms are used for channel learning based on observable channel state information. Deep Neural Network (DNN) is also employed to extract solutions for predicting beamforming vectors at the BS’s by taking mapping functions and uplink pilot signals into considerations.

In unsupervised Learning, where the user works with unlabeled data, various clustering techniques are applied to enhance network performance and connectivity without interruptions. K-means clustering reduces the data travel by storing data centers content into clusters. It optimizes the handover estimation based on mobility pattern and selection of relay nodes in the V2V network. Hierarchical clustering reduces network failure by detecting the intrusion in the mobile wireless network; unsupervised soft clustering helps in reducing latency by clustering fog nodes. The nonparametric Bayesian unsupervised learning technique reduces traffic in the network by actively serving the user’s requests and demands. Other unsupervised learning techniques such as Adversarial Auto Encoders (AAE) and Affinity Propagation Clustering techniques detect irregular behavior in the wireless spectrum and manage resources for ultradense small cells, respectively.

In case of an uncertain environment in the 5G wireless network, reinforcement learning (RL) techniques are employed to solve some problems. Actor-critic reinforcement learning is used for user scheduling and resource allocation in the network. Markov decision process (MDP) and Partially Observable MDP (POMDP) is used for Quality of Experience (QoE)-based handover decision-making for Hetnets. Controls packet call admission in HetNets and channel access process for secondary users in a Cognitive Radio Network (CRN). Deep RL is applied to decide the communication channel and mobility and speeds up the secondary user’s learning rate using an antijamming strategy. Deep RL is employed in various 5G network application parameters such as resource allocation and security [ 67 ]. Table 9 shows the state-of-the-art ML-based solution for 5G network.

The state-of-the-art ML-based solution for 5G network.

Highlights of machine learning techniques for 5G are as follows:

Pictorial representation of machine learning (ML) in 5G.

• In ML, a model will be defined which fulfills the desired requirements through which desired results are obtained. In the later stage, it examines accuracy from obtained results.
• ML plays a vital role in 5G network analysis for threat detection, network load prediction, final arrangement, and network formation. Searching for a better balance between power, length of antennas, area, and network thickness crossed with the spontaneous use of services in the universe of individual users and types of devices.

In [ 79 ], author’s firstly describes the demands for the traditional authentication procedures and benefits of intelligent authentication. The intelligent authentication method was established to improve security practice in 5G-and-beyond wireless communication systems. Thereafter, the machine learning paradigms for intelligent authentication were organized into parametric and non-parametric research methods, as well as supervised, unsupervised, and reinforcement learning approaches. As a outcome, machine learning techniques provide a new paradigm into authentication under diverse network conditions and unstable dynamics. In addition, prompt intelligence to the security management to obtain cost-effective, better reliable, model-free, continuous, and situation-aware authentication.

In [ 68 ], the authors proposed a machine learning-based model to predict the traffic load at a particular location. They used a mobile network traffic dataset to train a model that can calculate the total number of user requests at a time. To launch access and mobility management function (AMF) instances according to the requirement as there were no predictions of user request the performance automatically degrade as AMF does not handle these requests at a time. Earlier threshold-based techniques were used to predict the traffic load, but that approach took too much time; therefore, the authors proposed RNN algorithm-based ML to predict the traffic load, which gives efficient results.

In [ 15 ], authors discussed the issue of network slice admission, resource allocation among subscribers, and how to maximize the profit of infrastructure providers. The author proposed a network slice admission control algorithm based on SMDP (decision-making process) that guarantees the subscribers’ best acceptance policies and satisfiability (tenants). They also suggested novel N3AC, a neural network-based algorithm that optimizes performance under various configurations, significantly outperforms practical and straightforward approaches.

This section includes various works done on 5G ML by different authors. Table 10 shows the state-of-the-art work on the improvement of various parameters such as energy efficiency, Quality of Services (QoS), and latency with 5G ML.

The state-of-the-art ML-based approaches in 5G technology.

4.6. Optimization Techniques for 5G

Optimization techniques may be applied to capture NP-Complete or NP-Hard problems in 5G technology. This section briefly describes various research works suggested for 5G technology based on optimization techniques.

In [ 80 ], Massive MIMO technology is used in 5G mobile network to make it more flexible and scalable. The MIMO implementation in 5G needs a significant number of radio frequencies is required in the RF circuit that increases the cost and energy consumption of the 5G network. This paper provides a solution that increases the cost efficiency and energy efficiency with many radio frequency chains for a 5G wireless communication network. They give an optimized energy efficient technique for MIMO antenna and mmWave technologies based 5G mobile communication network. The proposed Energy Efficient Hybrid Precoding (EEHP) algorithm to increase the energy efficiency for the 5G wireless network. This algorithm minimizes the cost of an RF circuit with a large number of RF chains.

In [ 16 ], authors have discussed the growing demand for energy efficiency in the next-generation networks. In the last decade, they have figured out the things in wireless transmissions, which proved a change towards pursuing green communication for the next generation system. The importance of adopting the correct EE metric was also reviewed. Further, they worked through the different approaches that can be applied in the future for increasing the network’s energy and posed a summary of the work that was completed previously to enhance the energy productivity of the network using these capabilities. A system design for EE development using relay selection was also characterized, along with an observation of distinct algorithms applied for EE in relay-based ecosystems.

In [ 81 ], authors presented how AI-based approach is used to the setup of Self Organizing Network (SON) functionalities for radio access network (RAN) design and optimization. They used a machine learning approach to predict the results for 5G SON functionalities. Firstly, the input was taken from various sources; then, prediction and clustering-based machine learning models were applied to produce the results. Multiple AI-based devices were used to extract the knowledge analysis to execute SON functionalities smoothly. Based on results, they tested how self-optimization, self-testing, and self-designing are done for SON. The author also describes how the proposed mechanism classifies in different orders.

In [ 82 ], investigators examined the working of OFDM in various channel environments. They also figured out the changes in frame duration of the 5G TDD frame design. Subcarrier spacing is beneficial to obtain a small frame length with control overhead. They provided various techniques to reduce the growing guard period (GP) and cyclic prefix (CP) like complete utilization of multiple subcarrier spacing, management and data parts of frame at receiver end, various uses of timing advance (TA) or total control of flexible CP size.

This section includes various works that were done on 5G optimization by different authors. Table 11 shows how other authors worked on the improvement of multiple parameters such as energy efficiency, power optimization, and latency with 5G optimization.

Summary of Optimization Based Approaches in 5G Technology.

5. Description of Novel 5G Features over 4G

This section presents descriptions of various novel features of 5G, namely, the concept of small cell, beamforming, and MEC.

5.1. Small Cell

Small cells are low-powered cellular radio access nodes which work in the range of 10 meters to a few kilometers. Small cells play a very important role in implementation of the 5G wireless network. Small cells are low power base stations which cover small areas. Small cells are quite similar with all the previous cells used in various wireless networks. However, these cells have some advantages like they can work with low power and they are also capable of working with high data rates. Small cells help in rollout of 5G network with ultra high speed and low latency communication. Small cells in the 5G network use some new technologies like MIMO, beamforming, and mmWave for high speed data transmission. The design of small cells hardware is very simple so its implementation is quite easier and faster. There are three types of small cell tower available in the market. Femtocells, picocells, and microcells [ 83 ]. As shown in the Table 12 .

Types of Small cells.

MmWave is a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave has lots of advantages, but it has some disadvantages, too, such as mmWave signals are very high-frequency signals, so they have more collision with obstacles in the air which cause the signals loses energy quickly. Buildings and trees also block MmWave signals, so these signals cover a shorter distance. To resolve these issues, multiple small cell stations are installed to cover the gap between end-user and base station [ 18 ]. Small cell covers a very shorter range, so the installation of a small cell depends on the population of a particular area. Generally, in a populated place, the distance between each small cell varies from 10 to 90 meters. In the survey [ 20 ], various authors implemented small cells with massive MIMO simultaneously. They also reviewed multiple technologies used in 5G like beamforming, small cell, massive MIMO, NOMA, device to device (D2D) communication. Various problems like interference management, spectral efficiency, resource management, energy efficiency, and backhauling are discussed. The author also gave a detailed presentation of all the issues occurring while implementing small cells with various 5G technologies. As shown in the Figure 7 , mmWave has a higher range, so it can be easily blocked by the obstacles as shown in Figure 7 a. This is one of the key concerns of millimeter-wave signal transmission. To solve this issue, the small cell can be placed at a short distance to transmit the signals easily, as shown in Figure 7 b.

Pictorial representation of communication with and without small cells.

5.2. Beamforming

Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [ 84 ].

Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [ 21 ], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [ 85 ]. Figure 8 shows the pictorial representation of communication with and without using beamforming.

Pictorial Representation of communication with and without using beamforming.

5.3. Mobile Edge Computing

Mobile Edge Computing (MEC) [ 24 ]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [ 86 ]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user’s device. Therefore, the user need not download the complete application on his device that increases the performance of the end user’s device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [ 23 , 87 ], the author’s investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9 .

Pictorial representation of cloud computing vs. mobile edge computing.

6. 5G Security

Security is the key feature in the telecommunication network industry, which is necessary at various layers, to handle 5G network security in applications such as IoT, Digital forensics, IDS and many more [ 88 , 89 ]. The authors [ 90 ], discussed the background of 5G and its security concerns, challenges and future directions. The author also introduced the blockchain technology that can be incorporated with the IoT to overcome the challenges in IoT. The paper aims to create a security framework which can be incorporated with the LTE advanced network, and effective in terms of cost, deployment and QoS. In [ 91 ], author surveyed various form of attacks, the security challenges, security solutions with respect to the affected technology such as SDN, Network function virtualization (NFV), Mobile Clouds and MEC, and security standardizations of 5G, i.e., 3GPP, 5GPPP, Internet Engineering Task Force (IETF), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI). In [ 92 ], author elaborated various technological aspects, security issues and their existing solutions and also mentioned the new emerging technological paradigms for 5G security such as blockchain, quantum cryptography, AI, SDN, CPS, MEC, D2D. The author aims to create new security frameworks for 5G for further use of this technology in development of smart cities, transportation and healthcare. In [ 93 ], author analyzed the threats and dark threat, security aspects concerned with SDN and NFV, also their Commercial & Industrial Security Corporation (CISCO) 5G vision and new security innovations with respect to the new evolving architectures of 5G [ 94 ].

AuthenticationThe identification of the user in any network is made with the help of authentication. The different mobile network generations from 1G to 5G have used multiple techniques for user authentication. 5G utilizes the 5G Authentication and Key Agreement (AKA) authentication method, which shares a cryptographic key between user equipment (UE) and its home network and establishes a mutual authentication process between the both [ 95 ].

Access Control To restrict the accessibility in the network, 5G supports access control mechanisms to provide a secure and safe environment to the users and is controlled by network providers. 5G uses simple public key infrastructure (PKI) certificates for authenticating access in the 5G network. PKI put forward a secure and dynamic environment for the 5G network. The simple PKI technique provides flexibility to the 5G network; it can scale up and scale down as per the user traffic in the network [ 96 , 97 ].

Communication Security 5G deals to provide high data bandwidth, low latency, and better signal coverage. Therefore secure communication is the key concern in the 5G network. UE, mobile operators, core network, and access networks are the main focal point for the attackers in 5G communication. Some of the common attacks in communication at various segments are Botnet, message insertion, micro-cell, distributed denial of service (DDoS), and transport layer security (TLS)/secure sockets layer (SSL) attacks [ 98 , 99 ].

Encryption The confidentiality of the user and the network is done using encryption techniques. As 5G offers multiple services, end-to-end (E2E) encryption is the most suitable technique applied over various segments in the 5G network. Encryption forbids unauthorized access to the network and maintains the data privacy of the user. To encrypt the radio traffic at Packet Data Convergence Protocol (PDCP) layer, three 128-bits keys are applied at the user plane, nonaccess stratum (NAS), and access stratum (AS) [ 100 ].

7. Summary of 5G Technology Based on Above-Stated Challenges

In this section, various issues addressed by investigators in 5G technologies are presented in Table 13 . In addition, different parameters are considered, such as throughput, latency, energy efficiency, data rate, spectral efficiency, fairness & computing capacity, transmission rate, coverage, cost, security requirement, performance, QoS, power optimization, etc., indexed from R1 to R14.

Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).

8. Conclusions

This survey article illustrates the emergence of 5G, its evolution from 1G to 5G mobile network, applications, different research groups, their work, and the key features of 5G. It is not just a mobile broadband network, different from all the previous mobile network generations; it offers services like IoT, V2X, and Industry 4.0. This paper covers a detailed survey from multiple authors on different technologies in 5G, such as massive MIMO, Non-Orthogonal Multiple Access (NOMA), millimeter wave, small cell, MEC (Mobile Edge Computing), beamforming, optimization, and machine learning in 5G. After each section, a tabular comparison covers all the state-of-the-research held in these technologies. This survey also shows the importance of these newly added technologies and building a flexible, scalable, and reliable 5G network.

9. Future Findings

This article covers a detailed survey on the 5G mobile network and its features. These features make 5G more reliable, scalable, efficient at affordable rates. As discussed in the above sections, numerous technical challenges originate while implementing those features or providing services over a 5G mobile network. So, for future research directions, the research community can overcome these challenges while implementing these technologies (MIMO, NOMA, small cell, mmWave, beam-forming, MEC) over a 5G network. 5G communication will bring new improvements over the existing systems. Still, the current solutions cannot fulfill the autonomous system and future intelligence engineering requirements after a decade. There is no matter of discussion that 5G will provide better QoS and new features than 4G. But there is always room for improvement as the considerable growth of centralized data and autonomous industry 5G wireless networks will not be capable of fulfilling their demands in the future. So, we need to move on new wireless network technology that is named 6G. 6G wireless network will bring new heights in mobile generations, as it includes (i) massive human-to-machine communication, (ii) ubiquitous connectivity between the local device and cloud server, (iii) creation of data fusion technology for various mixed reality experiences and multiverps maps. (iv) Focus on sensing and actuation to control the network of the entire world. The 6G mobile network will offer new services with some other technologies; these services are 3D mapping, reality devices, smart homes, smart wearable, autonomous vehicles, artificial intelligence, and sense. It is expected that 6G will provide ultra-long-range communication with a very low latency of 1 ms. The per-user bit rate in a 6G wireless network will be approximately 1 Tbps, and it will also provide wireless communication, which is 1000 times faster than 5G networks.

Acknowledgments

Author contributions.

Conceptualization: R.D., I.Y., G.C., P.L. data gathering: R.D., G.C., P.L, I.Y. funding acquisition: I.Y. investigation: I.Y., G.C., G.P. methodology: R.D., I.Y., G.C., P.L., G.P., survey: I.Y., G.C., P.L, G.P., R.D. supervision: G.C., I.Y., G.P. validation: I.Y., G.P. visualization: R.D., I.Y., G.C., P.L. writing, original draft: R.D., I.Y., G.C., P.L., G.P. writing, review, and editing: I.Y., G.C., G.P. All authors have read and agreed to the published version of the manuscript.

This paper was supported by Soonchunhyang University.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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• Published: 16 May 2024

The Egyptian pyramid chain was built along the now abandoned Ahramat Nile Branch

• Eman Ghoneim   ORCID: orcid.org/0000-0003-3988-0335 1 ,
• Timothy J. Ralph   ORCID: orcid.org/0000-0002-4956-606X 2 ,
• Suzanne Onstine 3 ,
• Raghda El-Behaedi 4 ,
• Gad El-Qady 5 ,
• Amr S. Fahil 6 ,
• Mahfooz Hafez 5 ,
• Magdy Atya 5 ,
• Mohamed Ebrahim   ORCID: orcid.org/0000-0002-4068-5628 5 ,
• Ashraf Khozym 5 &
• Mohamed S. Fathy 6  

Communications Earth & Environment volume  5 , Article number:  233 ( 2024 ) Cite this article

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• Archaeology
• Geomorphology
• Hydrogeology
• Sedimentology

The largest pyramid field in Egypt is clustered along a narrow desert strip, yet no convincing explanation as to why these pyramids are concentrated in this specific locality has been given so far. Here we use radar satellite imagery, in conjunction with geophysical data and deep soil coring, to investigate the subsurface structure and sedimentology in the Nile Valley next to these pyramids. We identify segments of a major extinct Nile branch, which we name The Ahramat Branch, running at the foothills of the Western Desert Plateau, where the majority of the pyramids lie. Many of the pyramids, dating to the Old and Middle Kingdoms, have causeways that lead to the branch and terminate with Valley Temples which may have acted as river harbors along it in the past. We suggest that The Ahramat Branch played a role in the monuments’ construction and that it was simultaneously active and used as a transportation waterway for workmen and building materials to the pyramids’ sites.

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Introduction.

The landscape of the northern Nile Valley in Egypt, between Lisht in the south and the Giza Plateau in the north, was subject to a number of environmental and hydrological changes during the past few millennia 1 , 2 . In the Early Holocene (~12,000 years before present), the Sahara of North Africa transformed from a hyper-arid desert to a savannah-like environment, with large river systems and lake basins 3 , 4 due to an increase in global sea level at the end of the Last Glacial Maximum (LGM). The wet conditions of the Sahara provided a suitable habitat for people and wildlife, unlike in the Nile Valley, which was virtually inhospitable to humans because of the constantly higher river levels and swampy environment 5 . At this time, Nile River discharge was high, which is evident from the extensive deposition of organic-rich fluvial sediment in the Eastern Mediterranean basin 6 . Based on the interpretation of archeological material and pollen records, this period, known as the African Humid Period (AHP) (ca. 14,500–5000 years ago), was the most significant and persistent wet period from the early to mid-Holocene in the eastern Sahara region 7 , with an annual rainfall rate of 300–920 mm yr −1   8 . During this time the Nile would have had several secondary channels branching across the floodplain, similar to those described by early historians (e.g., Herodotus).

During the mid-Holocene (~10,000–6000 years ago), freshwater marshes were common within the Nile floodplain causing habitation to be more nucleated along the desert margins of the Nile Valley 9 . The desert margins provided a haven from the high Nile water. With the ending of the AHP and the beginning of the Late Holocene (~5500 years ago to present), rainfall greatly declined, and the region’s humid phase gradually came to an end with punctuated short wet episodes 10 . Due to increased aridity in the Sahara, more people moved out of the desert towards the Nile Valley and settled along the edge of the Nile floodplain. With the reduced precipitation, sedimentation increased in and around the Nile River channels causing the proximal floodplain to rise in height and adjacent marshland to decrease in the area 11 , 12 estimated the Nile flood levels to have ranged from 1 to 4 m above the baseline (~5000 BP). Inhabitants moved downhill to the Nile Valley and settled in the elevated areas on the floodplain, including the raised natural levees of the river and jeziras (islands). This was the beginning of the Old Kingdom Period (ca. 2686 BCE) and the time when early pyramid complexes, including the Step Pyramid of Djoser, were constructed at the margins of the floodplain. During this time the Nile discharge was still considerably higher than its present level. The high flow of the river, particularly during the short-wet intervals, enabled the Nile to maintain multiple branches, which meandered through its floodplain. Although the landscape of the Nile floodplain has greatly transformed due to river regulation associated with the construction of the Aswan High Dam in the 1960s, this region still retains some clear hydro-geomorphological traces of the abandoned river channels.

Since the beginning of the Pharaonic era, the Nile River has played a fundamental role in the rapid growth and expansion of the Egyptian civilization. Serving as their lifeline in a largely arid landscape, the Nile provided sustenance and functioned as the main water corridor that allowed for the transportation of goods and building materials. For this reason, most of the key cities and monuments were in close proximity to the banks of the Nile and its peripheral branches. Over time, however, the main course of the Nile River laterally migrated, and its peripheral branches silted up, leaving behind many ancient Egyptian sites distant from the present-day river course 9 , 13 , 14 , 15 . Yet, it is still unclear as to where exactly the ancient Nile courses were situated 16 , and whether different reaches of the Nile had single or multiple branches that were simultaneously active in the past. Given the lack of consensus amongst scholars regarding this subject, it is imperative to develop a comprehensive understanding of the Nile during the time of the ancient Egyptian civilization. Such a poor understanding of Nile River morphodynamics, particularly in the region that hosts the largest pyramid fields of Egypt, from Lisht to Giza, limits our understanding of how changes in the landscape influenced human activities and settlement patterns in this region, and significantly restricts our ability to understand the daily lives and stories of the ancient Egyptians.

Currently, much of the original surface of the ancient Nile floodplain is masked by either anthropogenic activity or broad silt and sand sheets. For this reason, singular approaches such as on-ground searches for the remains of hidden former Nile branches are both increasingly difficult and inauspicious. A number of studies have already been carried out in Egypt to locate segments of the ancient Nile course. For instance 9 , proposed that the axis of the Nile River ran far west of its modern course past ancient cities such as el-Ashmunein (Hermopolis) 13 . mapped the ancient hydrological landscape in the Luxor area and estimated both an eastward and westward Nile migration rate of 2–3 km per 1000 years. In the Nile Delta region 17 , detected several segments of buried Nile distributaries and elevated mounds using geoelectrical resistivity surveys. Similarly, a study by Bunbury and Lutley 14 identified a segment of an ancient Nile channel, about 5000 years old, near the ancient town of Memphis ( men-nefer ). More recently 15 , used cores taken around Memphis to reveal a section of a lateral ancient Nile branch that was dated to the Neolithic and Predynastic times (ca. 7000–5000 BCE). On the bank of this branch, Memphis, the first capital of unified Egypt, was founded in early Pharaonic times. Over the Dynastic period, this lateral branch then significantly migrated eastwards 15 . A study by Toonen et al. 18 , using borehole data and electrical resistivity tomography, further revealed a segment of an ancient Nile branch, dating to the New Kingdom Period, situated near the desert edge west of Luxor. This river branch would have connected important localities and thus played a significant role in the cultural landscape of this area. More recent research conducted further north by Sheisha et al. 2 , near the Giza Plateau, indicated the presence of a former river and marsh-like environment in the floodplain east of the three great Pyramids of Giza.

Even though the largest concentration of pyramids in Egypt are located along a narrow desert strip from south Lisht to Giza, no explanation has been offered as to why these pyramid fields were condensed in this particular area. Monumental structures, such as pyramids and temples, would logically be built near major waterways to facilitate the transportation of their construction materials and workers. Yet, no waterway has been found near the largest pyramid field in Egypt, with the Nile River lying several kilometers away. Even though many efforts to reconstruct the ancient Nile waterways have been conducted, they have largely been confined to small sites, which has led to the mapping of only fragmented sections of the ancient Nile channel systems.

In this work, we present remote sensing, geomorphological, soil coring and geophysical evidence to support the existence of a long-lost ancient river branch, the Ahramat Branch, and provide the first map of the paleohydrological setting in the Lisht-Giza area. The finding of the Ahramat Branch is not only crucial to our understanding of why the pyramids were built in these specific geographical areas, but also for understanding how the pyramids were accessed and constructed by the ancient population. It has been speculated by many scholars that the ancient Egyptians used the Nile River for help transporting construction materials to pyramid building sites, but until now, this ancient Nile branch was not fully uncovered or mapped. This work can help us better understand the former hydrological setting of this region, which would in turn help us learn more about the environmental parameters that may have influenced the decision to build these pyramids in their current locations during the time of Pharaonic Egypt.

Position and morphology of the Ahramat Branch

Synthetic Aperture Radar (SAR) imagery and radar high-resolution elevation data for the Nile floodplain and its desert margins, between south Lisht and the Giza Plateau area, provide evidence for the existence of segments of a major ancient river branch bordering 31 pyramids dating from the Old Kingdom to Second Intermediate Period (2686−1649 BCE) and spanning between Dynasties 3–13 (Fig.  1a ). This extinct branch is referred to hereafter as the Ahramat Branch, meaning the “Pyramids Branch” in Arabic. Although masked by the cultivated fields of the Nile floodplain, subtle topographic expressions of this former branch, now invisible in optical satellite data, can be traced on the ground surface by TanDEM-X (TDX) radar data and the Topographic Position Index (TPI). Data analysis indicates that this lateral distributary channel lies between 2.5 and 10.25 km west from the modern Nile River. The branch appears to have a surface channel depth between 2 and 8 m, a channel length of about 64 km and a channel width of 200–700 m, which is similar to the width of the contemporary neighboring Nile course. The size and longitudinal continuity of the Ahramat Branch and its proximity to all the pyramids in the study area implies a functional waterway of great significance.

a Shows the Ahramat Branch borders a large number of pyramids dating from the Old Kingdom to the 2 nd Intermediate Period and spanning between Dynasties 3 and 13. b Shows Bahr el-Libeini canal and remnant of abandoned channel visible in the 1911 historical map (Egyptian Survey Department scale 1:50,000). c Bahr el-Libeini canal and the abandoned channel are overlain on satellite basemap. Bahr el-Libeini is possibly the last remnant of the Ahramat Branch before it migrated eastward. d A visible segment of the Ahramat Branch in TDX is now partially occupied by the modern Bahr el-Libeini canal. e A major segment of the Ahramat Branch, approximately 20 km long and 0.5 km wide, can be traced in the floodplain along the Western Desert Plateau south of the town of Jirza. Location of e is marked in white a box in a . (ESRI World Image Basemap, source: Esri, Maxar, Earthstar Geographics).

A trace of a 3 km river segment of the Ahramat Branch, with a width of about 260 m, is observable in the floodplain west of the Abu Sir pyramids field (Fig.  1b–d ). Another major segment of the Ahramat Branch, approximately 20 km long and 0.5 km wide can be traced in the floodplain along the Western Desert Plateau south of the town of Jirza (Fig.  1e ). The visible segments of the Ahramat Branch in TDX are now partially occupied by the modern Bahr el-Libeini canal. Such partial overlap between the courses of this canal, traced in the1911 historical maps (Egyptian Survey Department scale 1:50,000), and the Ahramat Branch is clear in areas where the Nile floodplain is narrower (Fig.  1b–d ), while in areas where the floodplain gets wider, the two water courses are about 2 km apart. In light of that, Bahr el-Libeini canal is possibly the last remnant of the Ahramat Branch before it migrated eastward, silted up, and vanished. In the course of the eastward migration over the Nile floodplain, the meandering Ahramat Branch would have left behind traces of abandoned channels (narrow oxbow lakes) which formed as a result of the river erosion through the neck of its meanders. A number of these abandoned channels can be traced in the 1911 historical maps near the foothill of the Western Desert plateau proving the eastward shifting of the branch at this locality (Fig.  1b–d ). The Dahshur Lake, southwest of the city of Dahshur, is most likely the last existing trace of the course of the Ahramat Branch.

Subsurface structure and sedimentology of the Ahramat Branch

Geophysical surveys using Ground Penetrating Radar (GPR) and Electromagnetic Tomography (EMT) along a 1.2 km long profile revealed a hidden river channel lying 1–1.5 m below the cultivated Nile floodplain (Fig.  2 ). The position and shape of this river channel is in an excellent match with those derived from radar satellite imagery for the Ahramat Branch. The EMT profile shows a distinct unconformity in the middle, which in this case indicates sediments that have a different texture than the overlying recent floodplain silt deposits and the sandy sediments that are adjacent to this former branch (Fig.  2 ). GPR overlapping the EMT profile from 600–1100 m on the transect confirms this. Here, we see evidence of an abandoned riverbed approximately 400 m wide and at least 25 m deep (width:depth ratio ~16) at this location. This branch has a symmetrical channel shape and has been infilled with sandy Neonile sediment different to other surrounding Neonile deposits and the underlying Eocene bedrock. The geophysical profile interpretation for the Ahramat Branch at this locality was validated using two sediment cores of depths 20 m (Core A) and 13 m (Core B) (Fig.  3 ). In Core A between the center and left bank of the former branch we found brown sandy mud at the floodplain surface and down to ~2.7 m with some limestone and chert fragments, a reddish sandy mud layer with gravel and handmade material inclusions at ~2.8 m, a gray sandy mud layer from ~3–5.8 m, another reddish sandy mud layer with gravel and freshwater mussel shells at ~6 m, black sandy mud from ~6–8 m, and sandy silt grading into clean, well-sorted medium sand dominated the profile from ~8 to >13 m. In Core B on the right bank of the former branch we found recently deposited brown sandy mud at the floodplain surface and down to ~1.5 m, alternating brown and gray layers of silty and sandy mud down to ~4 m (some reddish layers with gravel and handmade material inclusions), a black sandy mud layer from ~4–4.9 m, and another reddish sandy mud layer with gravel and freshwater mussel shells at ~5 m, before clean, well-sorted medium sand dominated the profile from 5 to >20 m. Shallow groundwater was encountered in both cores concurrently with the sand layers, indicating that the buried sedimentary structure of the abandoned Ahramat Branch acts as a conduit for subsurface water flow beneath the distal floodplain of the modern Nile River.

a Locations of geophysical profile and soil drilling (ESRI World Image Basemap, source: Esri, Maxar, Earthstar Geographics). Photos taken from the field while using the b Electromagnetic Tomography (EMT) and c Ground Penetrating Radar (GPR). d Showing the apparent conductivity profile, e showing EMT profile, and f showing GPR profiles with overlain sketch of the channel boundary on the GPR graph. g Simplified interpretation of the buried channel with the location of the two-soil coring of A and B.

It shows two-soil cores, A and core B, with soil profile descriptions, graphic core logs, sediment grain size charts, and example photographs.

Alignment of old and middle kingdom pyramids to the Ahramat Branch

The royal pyramids in ancient Egypt are not isolated monuments, but rather joined with several other structures to form complexes. Besides the pyramid itself, the pyramid complex includes the mortuary temple next to the pyramid, a valley temple farther away from the pyramid on the edge of a waterbody, and a long sloping causeway that connects the two temples. A causeway is a ceremonial raised walkway, which provides access to the pyramid site and was part of the religious aspects of the pyramid itself 19 . In the study area, it was found that many of the causeways of the pyramids run perpendicular to the course of the Ahramat Branch and terminate directly on its riverbank.

In Egyptian pyramid complexes, the valley temples at the end of causeways acted as river harbors. These harbors served as an entry point for the river borne visitors and ceremonial roads to the pyramid. Countless valley temples in Egypt have not yet been found and, therefore, might still be buried beneath the agricultural fields and desert sands along the riverbank of the Ahramat Branch. Five of these valley temples, however, partially survived and still exist in the study area. These temples include the valley temples of the Bent Pyramid, the Pyramid of Khafre, and the Pyramid of Menkaure from Dynasty 4; the valley temple of the Pyramid of Sahure from Dynasty 5, and the valley temple of the Pyramid of Pepi II from Dynasty 6. All the aforementioned temples are dated to the Old Kingdom. These five surviving temples were found to be positioned adjacent to the riverbank of the Ahramat Branch, which strongly implies that this river branch was contemporaneously functioning during the Old Kingdom, at the time of pyramid construction.

Analysis of the ground elevation of the 31 pyramids and their proximity to the floodplain, within the study area, helped explain the position and relative water level of the Ahramat Branch during the time between the Old Kingdom and Second Intermediate Period (ca. 2649–1540 BCE). Based on Fig. ( 4) , the Ahramat Branch had a high-water level during the first part of the Old Kingdom, especially during Dynasty 4. This is evident from the high ground elevation and long distance from the floodplain of the pyramids dated to that period. For instance, the remote position of the Bent and Red Pyramids in the desert, very far from the Nile floodplain, is a testament to the branch’s high-water level. On the contrary, during the Old Kingdom, our data demonstrated that the Ahramat Branch would have reached its lowest level during Dynasty 5. This is evident from the low altitudes and close proximity to the floodplain of most Dynasty 5 pyramids. The orientation of the Sahure Pyramid’s causeway (Dynasty 5) and the location of its valley temple in the low-lying floodplain provide compelling evidence for the relatively low water level proposition of the Ahramat Branch during this stage. The water level of the Ahramat Branch would have been slightly raised by the end of Dynasty 5 (the last 15–30 years), during the reign of King Unas and continued to rise during Dynasty 6. The position of Pepi II and Merenre Pyramids (Dynasty 6) deep in the desert, west of the Djedkare Isesi Pyramid (Dynasty 5), supports this notion.

It explains the position and relative water level of the Ahramat Branch during the time between the Old Kingdom and Second Intermediate Period. a Shows positive correlation between the ground elevation of the pyramids and their proximity to the floodplain. b Shows positive correlation between the average ground elevation of the pyramids and their average proximity to the floodplain in each Dynasty. c Illustrates the water level interpretation by Hassan (1986) in Faiyum Lake in correlation to the average pyramids ground elevation and average distances to the floodplain in each Dynasty. d The data indicates that the Ahramat Branch had a high-water level during the first period of the Old Kingdom, especially during Dynasty 4. The water level reduced afterwards but was raised slightly in Dynasty 6. The position of the Middle Kingdom’s pyramids, which was at lower altitudes and in close proximity to the floodplain as compared to those of the Old Kingdom might be explained by the slight eastward migration of the Ahramat Branch.

In addition, our analysis in Fig. ( 4) , shows that the Qakare Ibi Pyramid of Dynasty 8 was constructed very close to the floodplain on very low elevation, which implies that the Nile water levels were very low at this time of the First Intermediate Period (2181–2055 BCE). This finding is in agreement with previous work conducted by Kitchen 20 which implies that the sudden collapse of the Old Kingdom in Egypt (after 4160 BCE) was largely caused by catastrophic failure of the annual flood of the Nile River for a period of 30–40 years. Data from soil cores near Memphis indicated that the Old Kingdom settlement is covered by about 3 m of sand 11 . Accordingly, the Ahramat Branch was initially positioned further west during the Old Kingdom and then shifted east during the Middle Kingdom due to the drought-induced sand encroachments of the First Intermediate Period, “a period of decentralization and weak pharaonic rule” in ancient Egypt, spanning about 125 years (2181–2055 BCE) post Old Kingdom era. Soil cores from the drilling program at Memphis show dominant dry conditions during the First Intermediate Period with massive eolian sand sheets extended over a distance of at least 0.5 km from the edge of the western desert escarpment 21 . The Ahramat Branch continued to move east during the Second Intermediate Period until it had gradually lost most of its water supply by the New Kingdom.

The western tributaries of the Ahramat Branch

Sentinal-1 radar data unveiled several wide channels (inlets) in the Western Desert Plateau connected to the Ahramat Branch. These inlets are currently covered by a layer of sand, thus partially invisible in multispectral satellite imagery. In Sentinal-1 radar imagery, the valley floors of these inlets appear darker than the surrounding surfaces, indicating subsurface fluvial deposits. These smooth deposits appear dark owing to the specular reflection of the radar signals away from the receiving antenna (Fig.  5a, b ) 22 . Considering that Sentinel-1’s C-Band has a penetration capability of approximately 50 cm in dry sand surface 23 , this would suggest that the riverbed of these channels is covered by at least half a meter of desert sand. Unlike these former inlets, the course of the Ahramat Branch is invisible in SAR data due in large part to the presence of dense farmlands in the floodplain, which limits radar penetration and the detection of underlying fluvial deposition. Moreover, the radar topographic data from TDX revealed the areal extent of these inlets. Their river courses were extracted from TDX data using the Topographic Position Index (TPI), an algorithm which is used to compute the topographic slope positions and to automate landform classifications (Fig.  5c, d ). Negative TPI values show the former riverbeds of the inlets, while positive TPI signify the riverbanks bordering them.

a Conceptual sketch of the dependence of surface roughness on the sensor wavelength λ (modified after 48 ). b Expected backscatter characteristics in sandy desert areas with buried dry riverbeds. c Dry channels/inlets masked by desert sand in the Dahshur area. d The channels’ courses were extracted using TPI. Negative TPI values highlight the courses of the channels while positive TPI signify their banks.

Analysis indicated that several of the pyramid’s causeways, from Dynasties 4 and 6, lead to the inlet’s riverbanks (Fig.  6 ). Among these pyramids, are the Bent Pyramid, the first pyramid built by King Snefru in Dynasty 4 and among the oldest, largest, and best preserved ancient Egyptian pyramids that predates the Giza Pyramids. This pyramid is situated at the royal necropolis of Dahshur. The position of the Bent Pyramid, deep in the desert, far from the modern Nile floodplain, remained unexplained by researchers. This pyramid has a long causeway (~700 m) that is paved in the desert with limestone blocks and is attached to a large valley temple. Although all the pyramids’ valley temples in Egypt are connected to a water body and served as the landing point of all the river-borne visitors, the valley temple of the Bent Pyramid is oddly located deep in the desert, very distant from any waterways and more than 1 km away from the western edge of the modern Nile floodplain. Radar data revealed that this temple overlooked the bank of one of these extinct channels (called Wadi al-Taflah in historical maps). This extinct channel (referred to hereafter as the Dahshur Inlet due to its geographical location) is more than 200 m wide on average (Fig.  6 ). In light of this finding, the Dahshur Inlet, and the Ahramat Branch, are thus strongly argued to have been active during Dynasty 4 and must have played an important role in transporting building materials to the Bent Pyramid site. The Dahshur Inlet could have also served the adjacent Red Pyramid, the second pyramid built by the same king (King Snefru) in the Dahshur area. Yet, no traces of a causeway nor of a valley temple has been found thus far for the Red Pyramid. Interestingly, pyramids in this site dated to the Middle Kingdom, including the Amenemhat III pyramid, also known as the Black Pyramid, White Pyramid, and Pyramid of Senusret III, are all located at least 1 km far to the east of the Dynasty 4 pyramids (Bent and Red) near the floodplain (Fig.  6 ), which once again supports the notion of the eastward shift of the Ahramat Branch after the Old Kingdom.

a The two inlets are presently covered by sand, thus invisible in optical satellite imagery. b Radar data, and c TDX topographic data reveal the riverbed of the Sakkara Inlet due to radar signals penetration capability in dry sand. b and c show the causeways of Pepi II and Merenre Pyramids, from Dynasty 6, leading to the Saqqara Inlet. The Valley Temple of Pepi II Pyramid overlooks the inlet riverbank, which indicates that the inlet, and thus Ahramat Branch, were active during Dynasty 6. d Radar data, and e TDX topographic data, reveal the riverbed of the Dahshur Inlet with the Bent Pyramid’s causeway of Dynasty 4 leading to the Inlet. The Valley Temple of the Bent Pyramid overlooks the riverbank of the Dahshur Inlet, which indicates that the inlet and the Ahramat Branch were active during Dynasty 4 of the Old Kingdom.

Radar satellite data revealed yet another sandy buried channel (tributary), about 6 km north of the Dahshur Inlet, to the west of the ancient city of Memphis. This former fluvial channel (referred to hereafter as the Saqqara Inlet due to its geographical location) connects to the Ahramat Branch with a broad river course of more than 600 m wide. Data shows that the causeways of the two pyramids of Pepi II and Merenre, situated at the royal necropolis of Saqqara and dated to Dynasty 6, lead directly to the banks of the Saqqara Inlet (see Fig.  6 ). The 400 m long causeway of Pepi II pyramid runs northeast over the southern Saqqara plateau and connects to the riverbank of the Saqqara Inlet from the south. The causeway terminates with a valley temple that lies on the inlet’s riverbank. The 250 long causeway of the Pyramid of Merenre runs southeast over the northern Saqqara plateau and connects to the riverbank of the Saqqara Inlet from the north. Since both pyramids dated to Dynasty 6, it can be argued that the water level of the Ahramat Branch was higher during this period, which would have flooded at least the entrance of its western inlets. This indicates that the downstream segment of the Saqqara Inlet was active during Dynasty 6 and played a vital role in transporting construction materials and workers to the two pyramids sites. The fact that none of the Dynasty 5 pyramids in this area (e.g., the Djedkare Isesi Pyramid) were positioned on the Saqqara Inlet suggests that the water level in the Ahramat Branch was not high enough to enter and submerge its inlets during this period.

In addition, our data analysis clearly shows that the causeways of the Khafre, Menkaure, and Khentkaus pyramids, in the Giza Plateau, lead to a smaller but equally important river bay associated with the Ahramat Branch. This lagoon-like river arm is referred to here as the Giza Inlet (Fig.  7 ). The Khufu Pyramid, the largest pyramid in Egypt, seems to be connected directly to the river course of the Ahramat Branch (Fig.  7 ). This finding proves once again that the Ahramat Branch and its western inlets were hydrologically active during Dynasty 4 of the Old Kingdom. Our ancient river inlet hypothesis is also in accordance with earlier research, conducted on the Giza Plateau, which indicates the presence of a river and marsh-like environment in the floodplain east of the Giza pyramids 2 .

The causeways of the four Pyramids lead to an inlet, which we named the Giza Inlet, that connects from the west with the Ahramat Branch. These causeways connect the pyramids with valley temples which acted as river harbors in antiquity. These river segments are invisible in optical satellite imagery since they are masked by the cultivated lands of the Nile floodplain. The photo shows the valley temple of Khafre Pyramid (Photo source: Author Eman Ghoneim).

During the Old Kingdom Period, our analysis suggests that the Ahramat Branch had a high-water level during the first part, especially during Dynasty 4 whereas this water level was significantly decreased during Dynasty 5. This finding is in agreement with previous studies which indicate a high Nile discharge during Dynasty 4 (e.g., ref. 24 ). Sediment isotopic analysis of the Nile Delta indicated that Nile flows decrease more rapidly by the end of Dynasty 4 25 , in addition 26 reported that during Dynasties 5 and 6 the Nile flows were the lowest of the entire Dynastic period. This long-lost Ahramat Branch (possibly a former Yazoo tributary to the Nile) was large enough to carry a large volume of the Nile discharge in the past. The ancient channel segment uncovered by 1 , 15 west of the city of Memphis through borehole logs is most likely a small section of the large Ahramat Branch detected in this study. In the Middle Kingdom, although previous studies implied that the Nile witnessed abundant flood with occasional failures (e.g., ref. 27 ), our analysis shows that all the pyramids from the Middle Kingdom were built far east of their Old Kingdom counterparts, on lower altitudes and in close proximity to the floodplain as compared to those of the Old Kingdom. This paradox might be explained by the fact that the Ahramat Branch migrated eastward, slightly away from the Western Desert escarpment, prior to the construction of the Middle Kingdom pyramids, resulting in the pyramids being built eastward so that they could be near the waterway.

The eastward migration and abandonment of the Ahramat Branch could be attributed to gradual tilting of the Nile delta and floodplain in lower Egypt towards the northeast due to tectonic activity 28 . A topographic tilt such as this would have accelerated river movement eastward due to the river being located in the west at a relatively higher elevation of the floodplain. While near-channel floodplain deposition would naturally lead to alluvial ridge development around the active Ahramat Branch, and therefore to lower-lying tracts of adjacent floodplain to the east, regional tilting may explain the wholesale lateral migration of the river in that direction. The eastward migration and abandonment of the branch could also be ascribed to sand incursion due to the branch’s proximity to the Western Desert Plateau, where windblown sand is abundant. This would have increased sand deposition along the riverbanks and caused the river to silt up, particularly during periods of low flow. The region experienced drought during the First Intermediate Period, prior to the Middle Kingdom. In the area of Abu Rawash north 29 and Dahshur site 11 , settlements from the Early Dynastic and Old Kingdom were found to be covered by more than 3 m of desert sands. During this time, windblown sand engulfed the Old Kingdom settlements and desert sands extended eastward downhill over a distance of at least 0.5 km 21 . The abandonment of sites at Abusir (5 th Dynasty), where the early pottery-rich deposits are covered by wind-blown sand and then mud without sherds, can be used as evidence that the Ahramat Branch migrated eastward after the Old Kingdom. The increased sand deposition activity, during the end of the Old Kingdom, and throughout the First Intermediate Period, was most likely linked to the period of drought and desertification of the Sahara 30 . In addition, the reduced river discharge caused by decreased rainfall and increased aridity in the region would have gradually reduced the river course’s capacity, leading to silting and abandonment of the Ahramat Branch as the river migrated to the east.

The Dahshur, Saqqara, and Giza inlets, which were connected to the Ahramat Branch from the west, were remnants of past active drainage systems dated to the late Tertiary or the Pleistocene when rainwater was plentiful 31 . It is proposed that the downstream reaches of these former channels (wadis) were submerged during times of high-water levels of the Ahramat Branch, forming long narrow water arms (inlets) that gave a wedge-like shape to the western flank of the Ahramat Branch. During the Old Kingdom, the waters of these inlets would have flowed westward from the Ahramat Branch rather than from their headwaters. As the drought intensified during the First Intermediate Period, the water level of the Ahramat Branch was lowered and withdrew from its western inlets, causing them to silt up and eventually dry out. The Dahshur, Saqqara, and Giza inlets would have provided a bay environment where the water would have been calm enough for vessels and boats to dock far from the busy, open water of the Ahramat Branch.

Sediments from the Ahramat Branch riverbed, which were collected from the two deep soil cores (cores A and B), show an abrupt shift from well-sorted medium sands at depth to overlying finer materials with layers including gravel, shell, and handmade materials. This indicates a step-change from a relatively consistent higher-energy depositional regime to a generally lower-energy depositional regime with periodic flash floods at these sites. So, the Ahramat Branch in this region carried and deposited well-sorted medium sand during its last active phase, and over time became inactive, infilling with sand and mud until an abrupt change led the (by then) shallow depression fill with finer distal floodplain sediment (possibly in a wetland) that was utilized by people and experienced periodic flash flooding. Validation of the paleo-channel position and sediment type using these cores shows that the Ahramat Branch has similar morphological features and an upward-fining depositional sequence as that reported near Giza, where two cores were previously used to reconstruct late Holocene Nile floodplain paleo-environments 2 . Further deep soil coring could determine how consistent the geomorphological features are along the length of the Ahramat branch, and to help explain anomalies in areas where the branch has less surface expression and where remote sensing and geophysical techniques have limitations. Considering more core logs can give a better understanding of the floodplain and the buried paleo-channels.

The position of the Ahramat Branch along the western edge of the Nile floodplain suggests it to be the downstream extension of Bahr Yusef. In fact, Bahr Yusef’s course may have initially flowed north following the natural surface gradient of the floodplain before being forced to turn west to flow into the Fayum Depression. This assumption could be supported by the sharp westward bend of Bahr Yusef’s course at the entrance to the Fayum Depression, which could be a man-made attempt to change the waterflow direction of this branch. According to Römer 32 , during the Middle Kingdom, the Gadallah Dam located at the entrance of the Fayum, and a possible continuation running eastwards, blocked the flow of Bahr Yusef towards the north. However, a sluice, probably located near the village of el-Lahun, was created in order to better control the flow of water into the Fayum. When the sluice was locked, the water from Bahr Yusef was directed to the west and into the depression, and when the sluice was open, the water would flow towards the north via the course of the Ahramat Branch. Today, the abandoned Ahramat Branch north of Fayum appears to support subsurface water flow in the buried coarse sand bed layers, however these shallow groundwater levels are likely to be quite variable due to proximity of the bed layers to canals and other waterways that artificially maintain shallow groundwater. Groundwater levels in the region are known to be variable 33 , but data on shallow groundwater could be used to further validate the delineated paleo-channel of the Ahramat Branch.

The present work enabled the detection of segments of a major former Nile branch running at the foothills of the Western Desert Plateau, where the vast majority of the Ancient Egyptian pyramids lie. The enormity of this branch and its proximity to the pyramid complexes, in addition to the fact that the pyramids’ causeways terminate at its riverbank, all imply that this branch was active and operational during the construction phase of these pyramids. This waterway would have connected important locations in ancient Egypt, including cities and towns, and therefore, played an important role in the cultural landscape of the region. The eastward migration and abandonment of the Ahramat Branch could be attributed to gradual movement of the river to the lower-lying adjacent floodplain or tilting of the Nile floodplain toward the northeast as a result of tectonic activity, as well as windblown sand incursion due to the branch’s proximity to the Western Desert Plateau. The increased sand deposition was most likely related to periods of desertification of the Great Sahara in North Africa. In addition, the branch eastward movement and diminishing could be explained by the reduction of the river discharge and channel capacity caused by the decreased precipitation and increased aridity in the region, particularly during the end of the Old Kingdom.

The integration of radar satellite data with geophysical surveying and soil coring, which we utilized in this study, is a highly adaptable approach in locating similar former buried river systems in arid regions worldwide. Mapping the hidden course of the Ahramat Branch, allowed us to piece together a more complete picture of ancient Egypt’s former landscape and a possible water transportation route in Lower Egypt, in the area between Lisht and the Giza Plateau.

Revealing this extinct Nile branch can provide a more refined idea of where ancient settlements were possibly located in relation to it and prevent them from being lost to rapid urbanization. This could improve the protection measures of Egyptian cultural heritage. It is the hope that our findings can improve conservation measures and raise awareness of these sites for modern development planning. By understanding the landscape of the Nile floodplain and its environmental history, archeologists will be better equipped to prioritize locations for fieldwork investigation and, consequently, raise awareness of these sites for conservation purposes and modern development planning. Our finding has filled a much-needed knowledge gap related to the dominant waterscape in ancient Egypt, which could help inform and educate a wide array of global audiences about how earlier inhabitants were living and in what ways shifts in their landscape drove human activity in such an iconic region.

Materials and methods

The work comprised of two main elements: satellite remote sensing and historical maps and geophysical survey and sediment coring, complemented by archeological resources. Using this suite of investigative techniques provided insights into the nature and relationship of the former Ahramat Branch with the geographical location of the pyramid complexes in Egypt.

Satellite remote sensing and historical maps

Unlike optical sensors that image the land surface, radar sensors image the subsurface due to their unique ability to penetrate the ground and produce images of hidden paleo-rivers and structures. In this context, radar waves strip away the surface sand layer and expose previously unidentified buried channels. The penetration capability of radar waves in the hyper-arid regions of North Africa is well documented 4 , 34 , 35 , 36 , 37 . The penetration depth varies according to the radar wavelength used at the time of imaging. Radar signal penetration becomes possible without significant attenuation if the surface cover material is extremely dry (<1% moisture content), fine grained (<1/5 of the imaging wavelength) and physically homogeneous 23 . When penetrating desert sand, radar signals have the ability to detect subsurface soil roughness, texture, compactness, and dielectric properties 38 . We used the European Space Agency (ESA) Sentinel-1 data, a radar satellite constellation consisting of a C-Band synthetic aperture radar (SAR) sensor, operating at 5.405 GHz. The Sentinel-1 SAR image used here was acquired in a descending orbit with an interferometric wide swath mode (IW) at ground resolutions of 5 m × 20 m, and dual polarizations of VV + VH. Since Sentinal-1 is operated in the C-Band, it has an estimated penetration depth of 50 cm in very dry, sandy, loose soils 39 . We used ENVI v. 5.7 SARscape software for processing radar imagery. The used SAR processing sequences have generated geo-coded, orthorectified, terrain-corrected, noise free, radiometrically calibrated, and normalized Sentinel-1 images with a pixel size of 12.5 m. In SAR imagery subsurface fluvial deposits appear dark owing to specular reflection of the radar signals away from the receiving antenna, whereas buried coarse and compacted material, such as archeological remains appear bright due to diffuse reflection of radar signals 40 .

Other previous studies have shown that combining radar topographic imagery (e.g., Shuttle Radar Topography Mission-SRTM) with SAR images improves the extraction and delineation of mega paleo-drainage systems and lake basins concealed under present-day topographic signatures 3 , 4 , 22 , 41 . Topographic data represents a primary tool in investigating surface landforms and geomorphological change both spatially and temporally. This data is vital in mapping past river systems due to its ability to show subtle variations in landform morphology 37 . In low lying areas, such as the Nile floodplain, detailed elevation data can detect abandoned channels, fossilized natural levees, river meander scars and former islands, which are all crucial elements for reconstructing the ancient Nile hydrological network. In fact, the modern topography in many parts of the study area is still a good analog of the past landscape. In the present study, TanDEM-X (TDX) topographic data, from the German Aerospace Centre (DLR), has been utilized in ArcGIS Pro v. 3.1 software due to its fine spatial resolution of 0.4 arc-second ( ∼ 12 m). TDX is based on high frequency X-Band Synthetic Aperture Radar (SAR) (9.65 GHz) and has a relative vertical accuracy of 2 m for areas with a slope of ≤20% 42 . This data was found to be superior to other topographic DEMs (e.g., Shuttle Radar Topography Mission and ASTER Global Digital Elevation Map) in displaying fine topographic features even in the cultivated Nile floodplain, thus making it particularly well suited for this study. Similar archeological investigations using TDX elevation data in the flat terrains of the Seyhan River in Turkey and the Nile Delta 43 , 44 allowed for the detection of levees and other geomorphologic features in unprecedented spatial resolution. We used the Topographic Position Index (TPI) module of 45 with the TDX data by applying varying neighboring radiuses (20–100 m) to compute the difference between a cell elevation value and the average elevation of the neighborhood around that cell. TPI values of zero are either flat surfaces with minimal slope, or surfaces with a constant gradient. The TPI can be computed using the following expression 46 .

Where the scaleFactor is the outer radius in map units and Irad and Orad are the inner and outer radius of annulus in cells. Negative TPI values highlight abandoned riverbeds and meander scars, while positive TPI signify the riverbanks and natural levees bordering them.

The course of the Ahramat Branch was mapped from multiple data sources and used different approaches. For instance, some segments of the river course were derived automatically using the TPI approach, particularly in the cultivated floodplain, whereas others were mapped using radar roughness signatures specially in sandy desert areas. Moreover, a number of abandoned channel segments were digitized on screen from rectified historical maps (Egyptian Survey Department scale 1:50,000 collected on years 1910–1911) near the foothill of the Western Desert Plateau. These channel segments together with the former river course segments delineated from radar and topographic data were aggregated to generate the former Ahramat Branch. In addition to this and to ensure that none of the channel segments of the Ahramat Branch were left unmapped during the automated process, a systematic grid-based survey (through expert’s visual observation) was performed on the satellite data. Here, Landsat 8 and Sentinal-2 multispectral images, Sentinal-1 radar images and TDX topographic data were used as base layers, which were thoroughly examined, grid-square by grid-square (2*2 km per a square) at a full resolution, in order to identify small-scale fluvial landforms, anomalous agricultural field patterns and irregular ditches, and determine their spatial distributions. Here, ancient fluvial channels were identified using two key aspects: First, the sinuous geometry of natural and manmade features and, second the color tone variations in the satellite imagery. For example, clusters of contiguous pixels with darker tones and sinuous shapes may signify areas of a higher moisture content in optical imagery, and hence the possible existence of a buried riverbed. Stretching and edge detection were applied to enhance contrasts in satellite images brightness to enable the visualization of traces of buried river segments that would otherwise go unobserved. Lastly, all the pyramids and causeways in the study site, along with ancient harbors and valley temples, as indicators of preexisting river channels, were digitized from satellite data and available archeological resources and overlaid onto the delineated Ahramat Branch for geospatial analysis.

Geophysical survey and sediment coring

Geophysical measurements using Ground Penetrating Radar (GPR) and Electromagnetic Tomography (EMT) were utilized to map subsurface fluvial features and validate the satellite remote sensing findings. GPR is effective in detecting changes of dielectric constant properties of sediment layers, and its signal responses can be directly related to changes in relative porosity, material composition, and moisture content. Therefore, GPR can help in identifying transitional boundaries in subsurface layers. EMT, on the other hand, shows the variations and thickness of large-scale sedimentary deposits and is more useful in clay-rich soil than GPR. In summer 2022, a geophysical profile was measured using GPR and EMT units with a total length of approximately 1.2 km. The GPR survey was conducted with a central frequency antenna of 35 MHz and a trigger interval of 5 cm. The EMT survey was performed using the multi-frequency terrain conductivity (EM–34–3) measuring system with a spacing of 10–11 meters between stations. To validate the remote sensing and geophysical data, two sediment cores with depths of 20 m (Core A) and 13 m (Core B) were collected using a deep soil driller. These cores were collected from along the geophysical profile in the floodplain. Sieving and organic analysis were performed on the sediment samples at Tanta University sediment lab to extract information about grain size for soil texture and total organic carbon. In soil texture analysis medium to coarse sediment, such as sands, are typical for river channel sediments, loamy sand and sandy loam deposits can be interpreted as levees and crevasse splays, whereas fine texture deposits, such as silt loam, silty clay loam, and clay deposits, are representative of the more distal parts of the river floodplain 47 .

Data availability

Data for replicating the results of this study are available as supplementary files at: https://figshare.com/articles/journal_contribution/Pyramids_Elevations_and_Distances_xlsx/25216259 .

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Acknowledgements

This work was funded by NSF grant # 2114295 awarded to E.G., S.O. and T.R. and partially supported by Research Momentum Fund, UNCW, to E.G. TanDEM-X data was awarded to E.G. and R.E by the German Aerospace Centre (DLR) (contract # DEM_OTHER2886). Permissions for collecting soil coring and sampling were obtained from the Faculty of Science, Tanta University, Egypt by coauthors Dr. Amr Fhail and Dr. Mohamed Fathy. Bradley Graves at Macquarie University assisted with preparation of the sedimentological figures. Hamada Salama at NRIAG assisted with the GPR field data collection.

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Eman Ghoneim

School of Natural Sciences, Macquarie University, Macquarie, NSW, 2109, Australia

Timothy J. Ralph

Department of History, The University of Memphis, Memphis, TN, 38152-3450, USA

Suzanne Onstine

Near Eastern Languages and Civilizations, University of Chicago, Chicago, IL, 60637, USA

Raghda El-Behaedi

National Research Institute of Astronomy and Geophysics (NRIAG), Helwan, Cairo, 11421, Egypt

Gad El-Qady, Mahfooz Hafez, Magdy Atya, Mohamed Ebrahim & Ashraf Khozym

Geology Department, Faculty of Science, Tanta University, Tanta, 31527, Egypt

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Eman Ghoneim conceived the ideas, lead the research project, and conducted the data processing and interpretations. The manuscript was written and prepared by Eman Ghoneim. Timothy J. Ralph co-supervised the project, contributed to the geomorphological and sedimentological interpretations, edited the manuscript and the figures. Suzanne Onstine co-supervised the project, contributed to the archeological and historical interpretations, and edited the manuscript. Raghda El-Behaedi contributed to the remote sensing data processing and methodology and edited the manuscript. Gad El-Qady supervised the geophysical survey. Mahfooz Hafez, Magdy Atya, Mohamed Ebrahim, Ashraf Khozym designed, collected, and interpreted the GPR and EMT data. Amr S. Fahil and Mohamed S. Fathy supervised the soil coring, sediment analysis, drafted sedimentological figures and contributed to the interpretations. All authors reviewed the manuscript and participated in the fieldwork.

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Ghoneim, E., Ralph, T.J., Onstine, S. et al. The Egyptian pyramid chain was built along the now abandoned Ahramat Nile Branch. Commun Earth Environ 5 , 233 (2024). https://doi.org/10.1038/s43247-024-01379-7

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Received : 06 December 2023

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Published : 16 May 2024

DOI : https://doi.org/10.1038/s43247-024-01379-7

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Working Paper 24-04: Measuring Price Effects from Disasters Using Public Data: A Case Study of Hurricane Ian

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Natural disasters can disrupt housing markets, causing destruction to communities and distress to economic activity. To estimate the effects of disasters on home prices, publicly-available data on property damages are often used to classify “treated” properties. However, by design these data lack precise geospatial information, leading to measurement error in the treatment variable as aggregate measures must be used. We leverage leading difference-in-differences and synthetic control methodologies across various treatments and levels of geography to measure price effects with such data following Hurricane Ian’s unexpected landfall in southwest Florida during September 2022, coinciding with the state’s initial recovery from the COVID-19 pandemic. Empirical results suggest positive, time-varying price effects, though we place caveats on these results as there may be many mechanisms underway; our results should be interpreted as descriptive correlations and not causal effects for various reasons. Our main contribution is methodological, highlighting the importance of robustness checks, functional form, statistical techniques, and testing across different samples. Additionally, quicker access to high quality public data could enhance quantitatively-informed conversations on natural disaster effects.​

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