Current and Future Cellular Systems E-Book
113,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Comprehensive reference on the latest trends, solutions, challenges, and future directions of 5G communications and beyond
Current and Future Cellular Systems: Technologies, Applications, and Challenges covers the state of the art in architectures and solutions for 5G wireless communication and beyond. This book is unique because instead of focusing on singular topics, it considers various technologies being used in conjunction with 5G and beyond 5G technologies. All new and emerging technologies are covered, along with their problems and how quality of service (QoS) can be improved with respect to future requirements.
This book highlights the latest trends in resource allocation techniques due to different device (or user) characteristics, provides a special focus on wide bandwidth millimeter wave communications including circuitry, antennas, and propagation, and discusses the involvement of decision-making processes assisted by artificial intelligence/machine learning (AI/ML) in applications such as resource allocation, power allocation, QoS improvement, and autonomous vehicles. Readers will also learn to develop mathematical modeling, perform simulation setup, and configure parameters related to simulations.
Current and Future Cellular Systems includes information on:
- The Internet of Vehicles (IoV), covering requirements, challenges, and limitations of Cellular Vehicle-to-Everything (C-V2X) with Resource Allocation (RA) techniques
- Intelligent reflecting surfaces, unmanned aerial vehicles, power optimized frameworks, challenges in a sub-6 GHz band, and communication in a THz band
- The role of IoT in healthcare, agriculture, smart home applications, networking requirements, and the metaverse
- Quantum computing, cloud computing, spectrum sharing methods, and performance analysis of WiFi 6/7 for indoor and outdoor environments
Providing expansive yet accessible coverage of the subject by exploring both basic and advanced topics, Current and Future Cellular Systems serves as an excellent introduction to the fundamentals of 5G and its applications for graduate students, researchers, and industry professionals in the field of wireless communication technologies.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 499
Veröffentlichungsjahr: 2025
Ähnliche
Table of Contents
Cover
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Preface
Glossary
Introduction
1 Spectrum Sharing Schemes for 5G and Beyond in Wireless Communication
1.1 Introduction
1.2 Spectrum Sharing Technologies
1.3 Case Study and Performance Evaluation
1.4 Future Trends and Challenges
1.5 Conclusion
References
2 Synergizing 5G, IoT, and Deep Learning: Pioneering Technological Integration for a Connected Future
2.1 Introduction
2.2 Security Threats on 5G Network
2.3 Applications of 5G
2.4 Advanced Intrusion Detection Systems (IDS)
2.5 Integration of 5G-IoT-DL
2.6 Security Challenges
2.7 Role of ML and DL in 5G at Application and Infra Level
2.8 Conclusion
References
3 Driving Next Generation IoT with 5G and Beyond
3.1 Introduction
3.2 Need for Technological Advancement
3.3 Existing Wireless Technologies
3.4 Challenges in Existing Technologies
3.5 Towards 5G Communication
3.6 IoT and its Evolution
3.7 Role of 5G in IoT
3.8 Integration of 5G IoT with Other Technologies
3.9 Techniques to Improve the Performance of Wireless Networks
3.10 Performance Parameters of Next Generation Wireless Systems
3.11 Challenges and Future Directions
3.12 Conclusion
References
4 Emerging Communication Paradigms for 6G IoT: Challenges and Opportunities
4.1 Introduction
4.2 Internet-of-Things and its Evolution
4.3 Enabling 6G Technologies for IoT
4.4 Use Case Scenarios
4.5 Challenges Faced and the Solutions Offered
4.6 Conclusion
References
5 Securing the Internet of Things: Cybersecurity Challenges, Strategies, and Future Directions in the Era of 5G and Edge Computing
5.1 Introduction
5.2 Literature Review
5.3 Applications in IoT and Edge Computing
5.4 Cybersecurity Management System for IoT Environments
5.5 Current Cyber Security Strategies in IoT
5.6 IoT Cybersecurity’s Role in Reshaping Machine Learning
5.7 Real Life Scenario
5.8 Conclusions
References
6 Autonomous Systems for 5G Networks: A Comprehensive Analysis of Features Toward Generalization and Adaptability
6.1 Introduction
6.2 Survey Method
6.3 Background and Related Works
6.4 Discussion
6.5 Conclusion
References
Note
7 Integrated Trends, Opportunities, and Challenges of 5G and Internet of Things
7.1 Introduction
7.2 Requirements for Integration of 5G with IoT
7.3 Opportunities of 5G integrated IoT
7.4 Challenges of 5G Integrated IoT
7.5 Conclusion
References
8 Advancement in Resource Allocation for Future Generation of Communications
8.1 Introduction
8.2 Current Trends in Multiple Access Techniques
8.3 Scheduling Algorithms for 5G/Beyond 5G
8.4 Factors Influencing Scheduling Algorithms
8.5 Resource Allocation for 5G Ultra-Dense Networks
8.6 Conclusion
References
9 Next-Gen Networked Healthcare: Requirements and Challenges
9.1 Introduction
9.2 Applications
9.3 Technological Prerequisites
9.4 Challenges in 5G Integration in Healthcare
9.5 Conclusion
References
10 Dynamic Resource Orchestration for Computing, Data, and IoT in Networked Systems: A Data-Centric Approach
10.1 Introduction
10.2 Dynamic Resource Orchestration: Foundations
10.3 Computing in Networked Systems
10.4 Data-Centric Orchestration
10.5 IoT Integration
10.6 Methodologies for Dynamic Resource Orchestration
10.7 Case Studies
10.8 Conclusion
References
11 Cognitive Cellular Networks: Empowering Future Connectivity Through Artificial Intelligence
11.1 Introduction
11.2 Foundations of Cognitive Cellular Networks
11.3 AI Algorithms for Network Optimization
11.4 Reinforcement Learning in Autonomous Network Management
11.5 Applications of Cognitive Cellular Networks
11.6 Challenges and Future Directions
11.7 Conclusion
References
12 Enhancing Scalability and Performance in Networked Applications Through Smart Computing Resource Allocation
12.1 Introduction
12.2 Foundations of Smart Computing Resource Allocation
12.3 Dynamic Resource Allocation Strategies
12.4 Intelligent Load Balancing Techniques
12.5 Real-Time Monitoring and Feedback Mechanisms
12.6 Case Studies and Best Practices
12.7 Security and Privacy Considerations
12.8 Future Trends and Emerging Technologies
12.9 Conclusion
References
13 5G-Enabled Fusion: Navigating the Future Landscape of Cloud Computing, Internet of Things, and Recommender Systems
13.1 Basics of Cloud Computing
13.2 Internet of Things
13.3 5G Technology
13.4 Recommender System
13.5 Conclusion
References
14 Confluence of Cellular IoT and Data Science for Smart Application using 5G
14.1 Introduction
14.2 Data Science and Cellular IoT
14.3 Research Problems in Data Science for Cellular IoT
14.4 Sensors in Cellular IoT Smart Farming
14.5 Related Work
14.6 Data Science for Agriculture
14.7 Challenges Faced by Cellular IoT Application in Data Science
14.8 Proposed Model and its Discussion
14.9 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Country wise deployment of sub 6GHz frequency bands for 5G Network...
Table 1.2 Review works in Spectrum Sharing and Cognitive Radio.
Chapter 2
Table 2.1 Overview of 5G with requirements at user and infrastructure level....
Chapter 3
Table 3.1 Comparison of different existing wireless technologies.
Table 3.2 Comparison between 5G mmWave and sub 6 GHz technologies.
Table 3.3 Current state-of-the-art research in 5G and its integration with o...
Chapter 4
Table 4.1 Comparison of wireless technologies on the basis of different syst...
Chapter 5
Table 5.1 Existing literature of detection of DDoS attacks in IoT.
Chapter 6
Table 6.1 Timeline of autonomous vehicles development since 1920.
Table 6.2 Autonomous systems levels based on its features.
Table 6.3 Articles available on topic autonomous systems based on search.
Table 6.4 Autonomous systems scopus articles and its citation count.
Table 6.5 Comparison of architecture characteristics of automation system....
Table 6.6 Sensor performance comparison under predefined conditions.
Chapter 8
Table 8.1 Comparison of multiple access schemes from conventional to latest ...
Chapter 9
Table 9.1 Status and key technologies for 5G healthcare technological prereq...
Table 9.2 Challenges, status, solutions, and metrics in 5G healthcare integr...
Chapter 12
Table 12.1 Summary of resources allotted, technological situations, problems...
Table 12.2 Comparison of smart load balancing algorithms.
Chapter 14
Table 14.1 Difference between traditional data and cellular IoT data.
Table 14.2 Types of sensors used in agriculture.
Table 14.3 Challenges and actions to be taken.
List of Illustrations
Chapter 1
Figure 1.1 Machine learning in spectrum sharing.
Chapter 2
Figure 2.1 Different types of security threats.
Figure 2.2 Integration of 5G, IoT, and DL.
Figure 2.3 Conceptual working of 5G-DL.
Figure 2.4 ML/DL at with 5G.
Chapter 3
Figure 3.1 Features of 5G technology.
Figure 3.2 Aspects of 5G.
Figure 3.3 Key enabling technologies.
Figure 3.4 MIMO block diagram.
Figure 3.5 System model depicting massive MIMO.
Figure 3.6 A macro cell partitioned into various small cells.
Figure 3.7 An indoor communication scenario supported by VLC.
Figure 3.8 Five layered architecture of 5G IoT.
Figure 3.9 Digital twin.
Figure 3.10 Wireless communication model depicting channel estimation.
Figure 3.11 Channel estimation techniques.
Chapter 4
Figure 4.1 Technological evolution from 1G to 6G and role of 6G in various I...
Figure 4.2 Holographic principle.
Figure 4.3 A comprehensive 6G IoT framework.
Figure 4.4 Enabling 6G technologies for IoT.
Figure 4.5 Smart health care use case scenario.
Figure 4.6 An illustration of smart transportation system powered by 6G.
Figure 4.7 A smart factory scenario managing production, assembly, packaging...
Figure 4.8 Use of IoT in smart agriculture.
Figure 4.9 Use case scenario of 6G IoT in smart classroom enabling smart lea...
Figure 4.10 Smart city use case scenario.
Chapter 5
Figure 5.1 Internet of things.
Figure 5.2 Stratification in cybersecurity: exploring multilayered defenses....
Figure 5.3 Machine learning security architecture.
Figure 5.4 IoT-enabled artificial intelligence ecosystem.
Figure 5.5 Scenario visualization.
Chapter 6
Figure 6.1 IEEE Xplore search flow diagram.
Figure 6.2 Documents by year. Source: Scopus/With permission of Elsevier.
Figure 6.3 Documents by country. Source: Scopus/With permission of Elsevier....
Figure 6.4 Documents by subject area. Source: Scopus/With permission of Else...
Figure 6.5 The architecture of an autonomous waterborne transportation syste...
Figure 6.6 Sensor taxonomy diagram.
Figure 6.7 Functional architecture of an autonomous system.
Figure 6.8 Autonomous vehicle control system.
Figure 6.9 Swimlane diagram for autonomous system.
Chapter 7
Figure 7.1 Evolution of technology.
Chapter 8
Figure 8.1 Resource sharing through mutual cooperation.
Chapter 9
Figure 9.1 The technological prerequisites corresponding to each application...
Chapter 10
Figure 10.1 Effective architecture for resource allocation in networks.
Figure 10.2 Representation of dynamic resource allocation.
Figure 10.3 Representation of allocating dynamic resources in container-base...
Figure 10.4 Mobile edge cloud computing and big data analytics.
Figure 10.5 Data computing and resource manager.
Chapter 11
Figure 11.1 Overview of cellular network architecture.
Figure 11.2 Representation cognitive radio technology in communication.
Figure 11.3 Representation of cognitive spectrum sharing.
Figure 11.4 Dynamic AI modeling for cellular network.
Chapter 12
Figure 12.1 Representation of node and resource allocation structure.
Figure 12.2 Resource allocation in cloud environment.
Figure 12.3 Overview of allocating resources and scheduling tasks.
Figure 12.4 Representation of load balancing in networked environments.
Chapter 13
Figure 13.1 Cloud computing in varied devices [2]/With permission of John Wi...
Figure 13.2 Cloud computing stack [5]/With permission of McGraw Hill Educati...
Figure 13.3 Arising technologies in cloud computing [1–3].
Figure 13.4 Types of recommender system techniques [23–27].
Figure 13.5 Trends of integration of recommender system, cloud computing, Io...
Chapter 14
Figure 14.1 Data science solutions to be used for IoT system.
Figure 14.2 Relation between air humidity and air temperature.
Figure 14.3 Proposed framework to be used in smart farming.
Guide
Cover
Table of Contents
Title Page
Copyright
About the Editors
List of Contributors
Preface
Glossary
Introduction
Begin Reading
Index
End User License Agreement
Pages
ii
iii
iv
xvii
xviii
xix
xx
xxi
xxii
xxiii
xxv
xxvi
xxvii
xxviii
xxix
xxx
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief
Moeness Amin
Jón Atli Benediktsson
Adam Drobot
James Duncan
Ekram Hossain
Brian Johnson
Hai Li
James Lyke
Joydeep Mitra
Desineni Subbaram Naidu
Tony Q. S. Quek
Behzad Razavi
Thomas Robertazzi
Diomidis Spinellis
Current and Future Cellular Systems
Technologies, Applications, and Challenges
Edited by
Garima Chopra
Chitkara University Institute of Engineering and Technology
Chitkara University, Punjab, India
Suhaib Ahmed
Model Institute of Engineering and Technology
Jammu, J&K, India
Shalli Rani
Chitkara University Institute of Engineering and Technology
Chitkara University, Punjab, India
Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission.
Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com.
Library of Congress Cataloging-in-Publication Data Applied for:
Hardback ISBN: 9781394256044
Cover Design: WileyCover Image: © WD Ashari/Shutterstock
About the Editors
Garima Chopra
Dr. Garima Chopra is currently working as an assistant professor, Department of Electronics and Communication, Chitkara University, Punjab. She received the B Tech degree in Electronics and Communication from Shri Mata Vaishno Devi University, Jammu and Kashmir, India, in 2013 and MTech degree specialized in Communication and Information Technology (CIT) in Electronics and Communication Department from National Institute of Technology, Srinagar, Jammu and Kashmir, India, in 2016. She completed her PhD in Electronics and Communication Engineering at Shri Mata Vaishno Devi University, Jammu and Kashmir, India. She was a postdoctoral fellow at IIT Hyderabad from 2021 to 2022 (SERB-NPDF). Her research interest includes the security in emerging technologies of 5G wireless communication network. Currently she is doing her research work on security issues of Ultra Dense Network in Fifth Generation wireless communication. She also worked on the security issues of wireless communication and their security aspects.
Suhaib Ahmed
Dr. Suhaib Ahmed is currently working as an assistant professor and faculty-in-charge, Centre for Research, Innovation and Entrepreneurship, Model Institute of Engineering and Technology, Jammu, India. He received his BE degree in Electronics and Communication Engineering from University of Jammu, India, in 2012, MTech and PhD degrees in Electronics and Communication Engineering from Shri Mata Vaishno Devi University, Katra, India, in 2014 and 2019, respectively. His research interests include quantum-dot cellular automata, nano-electronics, VLSI, data converters, nanotechnology, wireless sensor networks, IoT, AI/ML, bioelectronics, and signal processing. Dr. Ahmed is a Senior Member of IEEE, Life Member of IETE, ISTE, and Computer Society of India. He has published around 90 research papers in reputed peer-reviewed journals of IEEE, Springer, Elsevier, Wiley, etc. and in various international/national conferences. He has also worked as PI/Co-PI in 5 sponsored projects from the Ministry of Education, Ministry of Communications, Government of India and Jammu & Kashmir Department of Science and Technology. He has also served as TPC in various conferences and served as reviewer in various journals/conferences. He is also a member of editorial boards for various Web of Science and SCOPUS indexed journals including Scientific Reports, Plos One, IET Quantum Communications, etc.
Shalli Rani
Dr. Shalli Rani completed her post doctorate from Manchester Metropolitan University, UK, in June, 2023. She is professor in Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India. She has 18+ years of teaching experience. She received MCA degree from Maharishi Dayanand University, Rohtak, in 2004 and the MTech degree in Computer Science from Janardan Rai Nagar Vidyapeeth University, Udaipur, in 2007, and PhD degree in Computer Applications from Punjab Technical University, Jalandhar, in 2017. Her main area of interest and research are Wireless Sensor Networks, Underwater Sensor networks, Machine Learning, and Internet of Things. She has published/accepted/presented more than 100+ papers in international journals/conferences (SCI+Scopus) and edited/authored five books with international publishers. She is serving as the associate editor of IEEE Future Directions Letters. She served as a guest editor in IEEE Transaction on Industrial Informatics, Hindawi, WCMC, and Elsevier IoT Journals. She has also served as reviewer in many repudiated journals of IEEE, Springer, Elsevier, IET, Hindawi, and Wiley. She has worked on Big Data, Underwater Acoustic Sensors, and IoT to show the importance of WSN in IoT applications. She received a Young Scientist Award in February 2014 from Punjab Science Congress, Lifetime Achievement Award, and Supervisor of the Year Award from Global Innovation and Excellence, 2021.
List of Contributors
Laith Abualigah
MEU Research Unit
Middle East University
Amman
Jordan
Aditya Soni
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Yasir Ahmad
Department of Information Technology & Security
College of Computer Science & Information Technology
Jazan University
Jazan
Kingdom of Saudi Arabia
Suhaib Ahmed
Department of Electronics and Communication Engineering
Model Institute of Engineering and Technology
Jammu
J&K
India
Sherif Tawfik Amin
Department of Computer Science
College of Computer Science & Information Technology
Jazan University
Jazan
Kingdom of Saudi Arabia
Himanshi Babbar
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Durga Shankar Baggam
Department of CSE
Gandhi Engineering College
Gandhi Vihar
Khurda
Odisha
India
and
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Aditya Bakshi
School of Technology Management and Engineering
Narsee Monjee Institute of Management Sciences
Chandigarh
India
Shailesh Pramod Bendale
Department of Computer Engineering
NBN Sinhgad School of Engineering
Pune
Maharashtra
India
Abhijit Chitre
Department Electronics and Telecommunication Engineering
Vishwakarma Institute of Information Technology
Pune
Maharashtra
India
Garima Chopra
Chitkara Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Araddhana Arvind Deshmukh
Department of Computer Science & Information Technology (Cyber Security)
Symbiosis Skill and Professional University
Kiwale
Pune
Maharashtra
India
Amol Dhumane
Computer Science and Engineering Department
Symbiosis Institute of Technology
Lavale
Pune
Maharashtra
India
Ekta Dixit
Department of Computer Science
S.S.D Women’s Institute of Technology
Bathinda
Punjab
India
Geetanshi
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Akhil Gupta
School of Electronics & Electrical Engineering
Lovely Professional Engineering
Phagwara
Punjab
India
Mohammad Alamgir Hossain
Department of Computer Science
College of Computer Science & Information Technology
Jazan University
Jazan
Kingdom of Saudi Arabia
Sheela Hundekari
MITCOM, MCA Department
MIT ADT University
Loni Kalbhor
Pune
Maharashtra
India
Brejesh Lall
Bharti School of Telecommunication Technology and Management
IIT Delhi
New Delhi
India
Suresh Limkar
Department of Artificial Intelligence & Data Science
AISSMS Institute of Information Technology
Pune
Maharashtra
India
Suresh Limkar
Department of Artificial Intelligence & Data Science
AISSMS Institute of Information Technology
Pune
Maharashtra
India
Cherry Mangla
Assistant Professor
Concordia University of Edmonton
Edmonton
AB
Canada
Harshit Manocha
Chitkara University Institute of Engineering and Technology
Chitkara University
Punjab
India
Arushi Pandey
School of Technology Management and Engineering
Narsee Monjee Institute of Management Sciences
Chandigarh
India
Ankita Rana
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Shalli Rani
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Shalli Rani
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Kanica Sachdev
Bharti School of Telecommunication Technology and Management
IIT Delhi
New Delhi
India
Ankita Sharma
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Sheetal Sharma
IT- Department
Goswami Ganesh Dutta Santana Dharma College
Chandigarh
India
Shishir Shrivastava
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Shruti
PG Depatment of Information Technology
Goswami Ganesh Dutta Sanatan Dharma College
Chandigarh
India
and
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Ashu Taneja
Chitkara University Institute of Engineering and Technology
Chitkara University
Rajpura
Punjab
India
Neeti Taneja
Department of Computer Science Engineering
Noida Institute of Engineering and Technology
Greater Noida
Uttar Pradesh
India
Kirti Wanjale
Department Computer Engineering
Vishwakarma Institute of Information Technology
Pune
Maharashtra
India
Preface
The explosive demand for high data rates by users, coupled with the rapid development of data-intensive applications, has pushed the boundaries of current cellular technologies. The evolution from 1G to 5G has been transformative, but it has also revealed the limitations of these systems in addressing the diverse needs of future networks. As we approach the era of 5G and beyond, there is a growing realization that these technologies are no longer confined to a single domain. They encompass a wide range of applications, each with unique requirements, bringing about new challenges that demand innovative solutions.
This book provides a comprehensive examination of emerging technologies and the challenges faced in the deployment of 5G and beyond. It highlights the latest trends in resource allocation, power optimization, and the role of artificial intelligence (AI) and machine learning (ML) in enhancing quality of service (QoS) and decision-making processes in complex systems such as autonomous vehicles, resource allocation, and power management. The content aims to address the architectural and deployment challenges posed by the increasing heterogeneity of devices and user demands, offering insights into how future networks can evolve to meet these requirements.
The scope of this book spans several key areas, beginning with an overview of the evolution of cellular communication, technological advancements from 1G to 5G, and the critical architectural challenges that remain. A detailed discussion on resource allocation and power optimization for 5G and beyond is provided, exploring techniques and solutions that address the growing complexity of modern cellular networks. Additionally, the growing role of AI and ML in 5G networks is examined, showcasing how these technologies can assist in areas like resource assignment, power optimization, and autonomous decision-making.
Furthermore, the book investigates into the cutting-edge domain of millimeter wave (mmWave) communication, outlining the latest trends and challenges in high-frequency communication, i.e. Terahertz frequencies (THz), systems. Moreover, it also discusses the evolving technologies and highlights the new applications in the 5G domain. This book not only provides a clear understanding of the current state of 5G technology but also inspires future research and innovation. It is our hope that this book will serve as a guide for both seasoned professionals and newcomers to the field, helping them navigate the complexities of future networks and contribute to the advancement of next-generation communication technologies.
Glossary
5G Network:
The fifth generation of mobile network technology offering enhanced speed, reliability, and connectivity for applications such as IoT, autonomous vehicles, and smart cities.
6G Network:
The anticipated sixth generation of wireless communication technology, expected to provide ultra-high data rates, low latency, and massive device connectivity by 2030.
IoT (Internet of Things):
A network of interconnected devices that communicate and exchange data autonomously, often used in industries such as healthcare, transportation, and home automation.
Edge Computing:
A computing paradigm that processes data near its source to reduce latency, improve efficiency, and enhance real-time decision-making capabilities.
Machine Learning (ML):
A branch of artificial intelligence that enables systems to learn and improve from data without explicit programming, used for predictive analytics and decision-making.
Deep Learning (DL):
A subset of machine learning involving neural networks with many layers, used for tasks such as intrusion detection and image recognition.
Spectrum Sharing:
A technique for efficient use of radio frequency spectrum by allowing multiple users or devices to share the same frequency bands dynamically.
Cognitive Radio:
A radio technology that intelligently detects unused spectrum and adjusts transmission parameters to optimize utilization without interfering with primary users.
Handover (HO):
A process in wireless communication where a mobile device switches connection from one base station to another to maintain seamless connectivity.
Distributed Denial of Service (DDoS):
A cyberattack where multiple systems overwhelm a target, such as a server or network, causing disruption of services.
Network Slicing:
A 5G feature that creates multiple virtual networks on a shared physical infrastructure to cater to specific use cases with different requirements.
Artificial Intelligence (AI):
The simulation of human intelligence processes by machines, especially computer systems, enabling tasks like learning, reasoning, and self-correction.
Cybersecurity:
The practice of protecting systems, networks, and data from digital attacks, unauthorized access, and damage.
Blockchain:
A decentralized digital ledger technology used for secure and transparent data recording and verification across distributed networks.
Non-orthogonal Multiple Access (NOMA):
A multiple access technique allowing multiple users to share the same resources (frequency/time) by allocating different power levels to optimize spectral efficiency.
Interference Mitigation:
Strategies and techniques to reduce or eliminate signal interference in wireless communication systems to improve performance.
Dynamic Spectrum Access (DSA):
A method that allows unlicensed users to access spectrum dynamically based on availability and usage patterns.
Introduction
The surging demand for high data rates and the rapid proliferation of data-intensive applications have pushed the boundaries of current cellular technologies. The evolution from the first-generation (1G) to fifth-generation (5G) mobile networks has been transformative, enabling faster speeds, lower latency, and broader connectivity. However, these advancements have also highlighted the limitations of existing systems in addressing the diverse needs of future networks. As we enter the 5G era and beyond, these technologies are no longer confined to a single domain; instead, they encompass a wide array of applications, each with unique requirements. This shift introduces unprecedented challenges that necessitate innovative solutions to meet the demands of an increasingly interconnected world.
This book delves into the emerging technologies and challenges shaping the deployment of 5G and beyond, offering a comprehensive examination of how these advancements can overcome the complexities of modern cellular systems. It addresses critical aspects of resource allocation, power optimization, and the integration of Artificial Intelligence (AI) and Machine Learning (ML) to enhance decision-making processes and Quality of Service (QoS). These technologies have proven instrumental in addressing complex problems in areas like autonomous systems, power management, and resource assignment.
This book is structured to provide readers with a clear understanding of the evolving technological landscape. It begins by tracing the evolution of cellular communication from 1G to 5G, highlighting the critical architectural challenges that persist. The discussion then delves into resource allocation and power optimization techniques, essential for managing the increasing complexity of modern networks.
A significant portion of this book explores the role of AI and ML in 5G networks, showcasing their potential to revolutionize resource management and autonomous decision-making. The book also delves into cutting-edge domains like millimeter wave (mmWave) and terahertz (THz) communication, highlighting the latest trends and challenges associated with high-frequency systems. Furthermore, it investigates emerging applications within the 5G domain, demonstrating how they are reshaping industries and enabling innovative use cases.
This book aims to not only provide a clear understanding of the current state of 5G technology but also to inspire future research and innovation in the field. By offering insights into the architectural and deployment challenges posed by the heterogeneity of devices and user demands, it seeks to guide both seasoned professionals and newcomers in navigating the complexities of future networks.
We hope this book serves as a valuable resource for those seeking to understand and contribute to the advancement of next-generation communication technologies, fostering a future of seamless, intelligent, and interconnected networks.
1Spectrum Sharing Schemes for 5G and Beyond in Wireless Communication
Aditya Bakshi1, Akhil Gupta2, and Arushi Pandey1
1School of Technology Management and Engineering, Narsee Monjee Institute of Management Studies, Chandigarh, India
2School of Electronics & Electrical Engineering, Lovely Professional Engineering, Phagwara, Punjab, India
1.1 Introduction
In last 10 years, a great advancement has been done in increasing the number of wireless broadband and multimedia devices after evolution of the first-generation (1G) mobile communication system. New services and use cases have evolved using fifth-generation (5G) wireless networks, namely, enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC) etc. The eMBB shows enhancements in user data rates whereas URLLC is used for mission-critical and time-sensitive applications which provide low-latency and ultra-high reliability. Enabling communication between a great number of devices can be achieved by massive machine-type communications (m-MTC). The evolution of the Internet of Things (IoT) and its subset, industrial IoT (IIoT) helps to generate efficient and sustainable production using ubiquitous Internet connections [1]. Beyond 5G, the next i.e. sixth-generation (6G) communication systems plays an important role in accelerating the IOT implementation and diverse industry areas. There is a 38% annual growth rate in Internet from 2016 to 2023 [2, 3], and mobile connectivity [2] is at peak in year 2022. Over 50 million devices around the world will be connected as a part of IOT by 2030.
So, a new solution must be implemented as available frequency resources are limited in 5G demand for bandwidth increases as new applications are growing very fast and the use of mobile data traffic also increases with time. The Internet traffic in 2030 will be 5000 exabytes as per the International Telecommunication Union (ITU) [4]. So, there is a need for revision in the utilization of supplementary frequency bands for higher spectrums. There are lots of challenges that exist in using Millimeter Wave (mmW) bands which hamper its large-scale implementation such as the behavior of mmWs spreading in different directions whenever changes occur in current cellular systems, mmW bands can be affected by multiple objects due to multiple objects, Rapid fluctuations in channels due to small coherence time of mmW bands, etc. The cellular network frequency bands are shown Table 1.1.
Table 1.1 Country wise deployment of sub 6GHz frequency bands for 5G Network.
Countries
3–4 GHz
4–5 GHz
5–7 GHz
China
3.3–3.6 GHz
4.5–5 GHz
UK
3.4–3.8 GHz
USA
3.7–3.98 GHz
4.49–4.99 GHz
5.9–7.1 GHz
Canada
3.65–4.0 GHz
5.9–7.1 GHz
India
3.4–3.6 GHz
Australia
3.34–3.7 GHz
Italy
3.6–3.8 GHz
Malaysia
3.5 GHz
Korea
3.4–3.7 GHz, 3.7–4.0 GHz
Japan
3.6–4.1GHz
4.5–4.9 GHz
EU
3.4–3.8 GHz
5.9–6.4 GHz
1.1.1 Motivation
Spectrum sharing facilitates the shared use of the spectrum as compared to a fixed allocation of spectrum where primary users can access the frequency bands with dedicated frequency portions under specific rules and conditions. Spectrum sharing may look trivial but characteristically it is very complex to implement. As Mobile Network Users (MNOs) are operating concurrently, spectrum sharing limits the activities of primary users on the band. So for participation in spectrum sharing schemes and viewing these participations, proper incentives are needed to motivate primary users that impact positive outcomes on the organization.
1.1.2 Literature Review
In literature, investigation has been done for spectrum sharing mechanism for CCN and CRSN. Review work for spectrum sharing and cognitive radio is shown in Table 1.2. The table covers description, advantages, and disadvantages of each article.
Table 1.2 Review works in Spectrum Sharing and Cognitive Radio.
Ref No.
Author
Description
Advantages
Disadvantages
[5]
Ali and Hamounda
Presented a noval spectrum sensing system with proper spectrum sensing methods and its classification. The standard spectrum sensing techniques challenges has also been discussed here
The interweave cognitive radio mode technology for the spectrum sensing for improving the system performance
In the presence of practical imperfections, the compressed sensing based approaches cannot give good results
[6]
Sharma, Lagunas et al.
This work provides a review of application of CS in CR communications and compare it with different state of art approaches
Various application areas such as wideband SS and REM construction has been acquired using RF parameter
Less channel occupancy, comprehensive estimation mismatch and realistic signal model evaluation
[7]
Kliks et al.
Presented an energy efficient CSS algorithm with news ideas of implementing energy efficient model
Reduction of processing node and energy consumption in each node
Quality of service (QOS) can be easily breach with less reliable spectrum scheme
[8]
Tanab and Hamounda
Presented a resource allocation algorithm and approaches for CRNs having certain criteria, techniques and network architecture
Proper resource allocation and its component for cognitive radio networks has been explained
Network mobility, channel models, hybrid users and security of spectrum sensing can be improved
[9]
Ahmad, Rehmani et al.
CR sensor networks (CRSNs) based resource allocation schemes designed and classified on optimization standards
Centralized resource allocation in CRSN with different scheme, classes, and categories
Distributed and cluster-based frameworks can also yield good results
[10]
Tsiropoulos, Dobre et al.
Review of dynamic spectrum allocation and aggregation with various optimized CRNs based resource allocation designs
Solve the problem of resource allocation using numerous design approaches
Difficult to adapt, reconfigure, accessible the allocation without CRN
[11]
Liang, Hanzo et al.
Presented a frequency-division-base and time-division-base channel implement on overlay spectrum access scheme in a cooperative cognitive radio (CCR) network setting
In CCN spectrum scheme both cooperative and non-cooperative games as well as matching games have been shown
Primary users (PUs) and cognitive users (CUs) only interacts with relaying in CCN not with game-theoretical sense
[12]
Tehrani, Vahid et al.
Review of characteristics of different authorization regimes with different coordination protocols, network topologies functional on licensed sharing scenarios
Reduces the latency by executing enhanced multi-band scheduling algorithms
Enhanced regulatory regimes and functional requirements is required if MTC services needs to be accommodated in spectrum
[13]
Voicu, Petrove et al.
Presented a wireless inter-technology model with a spectrum sharing mechanisms that incorporates a unified, system-level view the technical and non-technical aspects of spectrum
Integration of technical and non-technical aspects at different layers using technology circle
Performance of more than two broadband technologies and deployment of wide networks on inter-technology model without interference is still a big challenge
[14]
De Figueiredo, Jiao et al.
Author shows a radio-based framework with optimal use of radio spectrum to achieve improved spectrum utilization
Importance of a pivotal role in spectrum scarcity is explained using time-frequency resources based optimum utilization
Scalable OFDM-based air interface and its slots didn’t give fruitful results
Spectrum sharing facilitated by intellectual radio networks provides a more dynamic and efficient way of spectrum utilization. Intelligent radio enables intelligent autonomous devices to opportunistically sense, optimize, and utilize spectrum availability, improving spectrum efficiency and reducing spectrum scarcity issues Through spectrum sensing is capable of identifying unused or underused spectrum bands, making them more specialized -Reduces interference for users, enabling access to privileged access [15].
Different spectrum sharing models, including underlay, overlay and interweave, address different issues and regulatory requirements. The underlay enables secondary users to broadcast simultaneously with primary users under predetermined interruption limits, while the overlay enables users at the second can opportunistically use spectrum white spaces without harmful interference Primary users rely on interwave spectrum sensing to opportunistically access spectrum when spectrum is not in use [16].
Machine learning (ML) methods play an important role in spectrum sharing capabilities for spectrum sharing, especially in spectrum sensing, decision making, and resource allocation to improve spectrum sensing accuracy, optimize resource allocation, and learning algorithms supervised, unsupervised, reinforcement serves to enhance conscious radio decision-making strategies [17].
Resource allocation in notional radio networks requires optimal allocation of spectrum, power, and other network resources to optimize system performance, ensuring adequacy and efficiency Using method-based optimization, game theory, and ML algorithms are used to improve network efficiency to solve resource allocation challenges [18].
Spectrum sharing in multi-hop/multi-tier millimeter Wave (mmWave) networks is one of the most promising methods that could notably improve capacity and efficiency in such scenarios. Indeed, multiple devices will be allowed to use the same frequency band. Thus, effective scheduling algorithms are indispensable to minimize interference among nodes and provide equal access for all nodes. A scheduling algorithm for multi-hop mmWave networks minimizing transmission time while considering blockage and interference is proposed in [19]. The algorithm efficiently deals with these problems of the factors in mmWave communications. Cross-tier scheduling scheme for multi-tier mmWave wave wireless networks proposed for work in [20] is presented. The proposed scheme aims toward optimization of resource allocation and betterment of overall network performance by coordination of transmissions across different tiers. An optimal link scheduling algorithm for mmWave wave multi-hop networks equipped with multi-user MIMO radios is proposed for work in [21]. The algorithm attempts to maximize network throughput by taking into account the complexities of mmWave propagation and interference.
1.2 Spectrum Sharing Technologies
Spectrum-sharing technologies are an important part of for the effective functioning of 5G networks allowing for the effective use of available radio frequency spectrum, to support the anticipated massive increase in data traffic and connections. This technology would help allow for the existence of multiple wireless communications in the same frequency band, maximize management of spectrum efficiency, and help provide and support a wide range of services from high-speed broadband to IoT devices. Here are some of the key spectrums sharing [5] technologies and approaches being utilized and developed for 5G:
Dynamic Spectrum Sharing (DSS)
DSS enables 5G services to run in the same frequency ranges as 4G LTE by dynamically distributing spectrum resources in real-time based on demand. This enables operators to deploy 5G networks more quickly and efficiently, using existing spectrum allocations without the need for clearing bands or reassigning frequencies.
Licensed Shared Access (LSA)/Spectrum Access System (SAS)
LSA and SAS are frameworks that allow access to underutilized military and government-owned spectrum to commercial users allowing the use of dynamic database and spectrum sensing technologies to temporarily allocate a spectrum to 5G operators. This also helps increase the number of spectrums available without disturbing the incumbent users.
Citizens Broadband Radio Service (CBRS)
CBRS uses a 3-tier access system to manage spectrum sharing between incumbent users, giving priority access licenses, having general authorized access for users, and promoting efficient use of spectrum while protecting from harmful interference. It is a specific implementation of SAS in the United States operated in 3.5GHz band.
Millimeter Wave (mmWave) Spectrum Sharing
The mmWave frequency is gaining popularity as 5G is expands offering vast amounts of underutilized spectrum. Various techniques can be used for sharing these high frequency bands such as beamforming and directional antennas which can spatially isolate transmission ensuring the sharing of the same frequency band without interfering with other connections.
Cognitive Radio (CR)
Cognitive radio is a relatively advanced spectrum that can enhance the spectrum by causing an increase in its efficiency and dynamic interaction. CR can sense their surroundings, locate unoccupied frequency bands, and adjust their transmission parameters in real time to maximize available spectrum without disturbing the incumbent users
Coordinated Multi-Point (CoMP) Operation
CoMP can be used in crowded network environments for management interference and increasing efficiency. By coordinating signal transmission and reception across multiple base stations, networks can reduce interference while increasing the overall capacity and performance of 5G services.
Non-Orthogonal Multiple Access (NOMA)
By using varying different power levels and signal processing techniques NOMA enables users to share the same frequency bands improving the spectral efficiency and capacity of 5G networks. It also allows for a greater number of users on the same spectrum band.
Such solutions are only part of a bigger approach to meet 5G requirements which include high data rates, low latency and capacity to serve a large number of devices at the same time. With 5G advancing and new sharing spectrum methodologies and innovations getting created, further improvement will be seen in the efficiency and performance of the wireless networks.
1.2.1 Machine Learning in Spectrum Sharing
ML plays an important role in spectrum allocation, enabling decision-making and resource optimization in dynamic and complex wireless environments [22].
ML has been increasingly explored in the context of spectrum sharing, particularly in cognitive radio networks, where intelligent devices dynamically access and utilize available spectrum bands. Here are some existing works in this field along with references:
Q-Learning-based Spectrum Sharing
[23]
Reinforcement Learning for Spectrum Allocation
[24]
Deep Reinforcement Learning for Spectrum Management
[25]
Game Theory in Spectrum Sharing
[26]
Distributed Learning for Spectrum Sensing
[27]
Fuzzy Logic based Spectrum Allocation
[28]
Integrating ML into spectrum sharing can help algorithms predict, learn, and adapt to environments. Here are a few key applications and benefits of ML in spectrum sharing and also shown in Figure 1.1:
Predictive Spectrum Management
ML can help predict demand and adapt to different spectrum environments ensuring fast spectrum allocation and dynamic sharing. This can help optimize the user experience reducing interference and improving the service for the users.
Cognitive Radio Networks
Cognitive Radio with the help of ML can help find the most suitable channels and frequencies in the wireless spectrum without disturbing existing users. This improves overall performance and plays a key role in the complex process that supports 5G networks.
Figure 1.1 Machine learning in spectrum sharing.
Interference Management
ML algorithms can be developed to help identify all potential sources of interference in the frequency band and develop proactive prevention strategies. This is an important feature because it makes the network more reliable by ensuring the smooth operation of various connected devices.
Enhanced Security in Spectrum Sharing
Security is an important focus as with multiple users on the same frequency range there is a chance that others with malicious intent may misuse the information. ML can be used here to ensure that no interference occurs to detect any irregular patterns and deploy a timely response to prevent the security threat.
Resource Allocation and Optimization
ML can be used to improve the system for spectrum resource allocation, optimize it for the best performance, and fine-tune it according to the user’s behavioral patterns for improved efficiency. It calculates the appropriate timing for spectrum transfer and distributing bandwidth among users.
Signal Classification and Identification
ML can identify different signals and services received and classify them, distinguishing between the primary and secondary i.e. licensed and unlicenced users respectively. This facilitates proper functioning of the spectrum and prevention of interference in the spectrum signal.
Demand-Responsive Spectrum Access
The Demand-Responsive spectrum can be enabled across the spectrum with the help of ML adjusting allocation to the spectrum in real-time based on the patterns. This supports efficient and flexible spectrum sharing and plays an important role when spectrum demand fluctuates.
1.2.2 Cooperative and Cognitive Radio Networks
Networking and radio intelligence represent two major advances in wireless communications each meeting the growing need for more efficient use of spectrum, improving network performance and increasing wireless connectivity in an age of limited resources [6, 7]. Let’s take a closer look at both concepts to understand their capabilities, benefits, and how they complement each other in today’s wireless networks.
a) Cooperative Radio Networks
Cooperative wireless networks benefit from cooperation from various network nodes by nature in improving the elements of communication reliability, coverage extension, and overall performance in a wireless network. This is an essential principle because it presupposes that the nodes of the network act as relays that assist in transferring information to and from places. Important aspects concerning radio communications include [29, 30]:
Spatial Diversity:
It makes use of varied relay node types that act as space diversities for transmitting the signals to the spectrum. This is effective in reducing fading effects and promoting signal reliability caused by the weak strength of wireless communications.
Expanded Coverage:
Relays can be applied to increase the coverage’s scope that a network offers. More so, it remains very useful for emergency situations even in hard-to-reach areas.
Enhanced Spectral Efficiency:
As such, it encourages one communication and hence bigger efficiency with less power needed for the transmission of the signals, making more use of high frequencies feasible.
Enhanced Throughput and Reliability:
By combining signals from multiple paths, cooperative networks can achieve higher throughput and lower error rates compared to non-cooperative approaches.
Cognitive Radio Networks:
Cognitive power and expertise modification of transmission parameters based on temporal spectrum and user requirements lead to an improvement in the power of access and capacity of spectrum control.
Spectrum Sensing:
Radio information operationalized that the efficient use of spectrum took advantage of access to most times, the frequency bands left unused by primary users.
Spectrum Management:
It recommends the fitting of the most frequency bands for the business and consideration in respect of required specifications as to the availability.
Spectrum Mobility:
Cognitive radios can change their frequency of operation to switch between spectrum bands, ensuring seamless communication even when the current band becomes unavailable or suboptimal.
Spectrum Sharing:
It makes it more efficient and effective in that it’s dynamic sharing between users based on a pattern of usage and demand.
1.2.2.1 Integration of Cooperative and Cognitive Radio Networks
Radio technology and telecommunications can be used to improve the performance through direct communication and using dynamic spectrum [8, 31, 32]. This collaboration could lead to:
Boost Spectrum Reach and Efficiency:
While technological collaboration technologies can efficiently analyze spectrum even in a complicated spectrum environment, intelligence is able to detect underutilized spectrum.
Increased Communication Dependability:
It can be attained by integrating technical expertise and coordination, which can lower interference and signal loss and improve communication.
Adaptive Network Topology:
Based on the conditions of the network and the availability of spectrum, the network can modify its topology and communication quality to optimize coverage and performance.
The creation of communications services that satisfy present needs requires the integration of radio networks and radio intelligence for high speed, reliability and efficiency. As technology continues to evolve, the integration of these two approaches will play an important role in the effective management of spectrum and distribution of wireless communications resources.
1.2.3 Interference Mitigation Strategies
Interference mitigation gains significant importance in wireless communications due to increasing pressure on the demand for bandwidth and the proliferation of wireless devices in commercial as well as personal space. For instance, effective interference mitigation strategies are important for service delivery, spectral efficiency, and reliable communication in a congested environment [8, 9]. Following are some of the key strategies that help reduce the amount of interference in wireless networks [33, 34]:
Frequency Planning and Management
Dynamic Frequency Selection (DFS):
Through automatic selection of relatively less congested frequencies, DFS minimizes interference with other systems.
Spectrum Allocation:
It includes the frequency range that is being managed in such a way as to ensure there is minimal or no overlay; it has gone to the extent of assigning specific frequencies or groups of bands to different service providers or operators.
Power Control
Transmit Power Control:
In this system, the power is controlled in such a manner that one can reduce the level of interference being occasioned on other devices at the same time maintaining an acceptable quality standard of signal.
Adaptive Power Control:
Dynamically carried out by gaining the maximum link signal strength and reducing its interference in real time based on feedback of effectiveness.
Beamforming and MIMO (Multiple Input Multiple Output)
Beamforming:
It focuses patterns of the array of antennae electronic signals towards intended receivers only and makes interferences minimal to and from other unrelated ones.
MIMO:
Diversifies signals over multiple antennas at the transmitter and the destination. It uses the process of spatial multiplexing for communication that increases performance and decreases interference.
Cognitive Radio and Dynamic Spectrum Access
Spectrum Sensing:
There are techniques based on tracking spectrum holes or portions of the spectrum not being used by the licensed users and use these temporarily in such a way that there is no interference to the licensed user.
Dynamic Spectrum Access:
The system dynamically changes transmission parameters according to the spectrum environment to minimize interference.
Cooperative Communication
Relaying nodes used during communication so that they may cooperate to control the interference caused due to the better path of transmission and spatial diversity.
Interference Cancellation Techniques
Successive Interference Cancellation (SIC):
A receiver-side technique where the strongest interferers are decoded and subtracted one after the other, leaving at every stage a? weaker desired signal to be detected.
Joint Signal Processing:
At the receiver, advanced algorithms are used in separating colliding signals from different transmitters.
Inter-Cell Interference Coordination (ICIC) and eICIC for Cellular Networks
Inter-Cell Interference Coordination:
A mechanism used today with LTE and by all other cellular networks for controlling interferences among cells, for example, coordinating resource block usage.
Enhanced ICIC (eICIC):
It involves an added set of strategies like ABS (Almost Blank Subframe). Here the cells are, on some subframes, given the duty of reducing their transmission power so that interference with other users in neighboring cells may be reduced to the minimum.
Network Slicing and Virtualization
Network Slicing:
A mechanism of separating various service offerings by flexibly partitioning the physical infrastructure into many virtual network segments with dedicated resources for interference management.
Virtualization:
The deployment of two recent technologies, network function virtualization (NFV) and software-defined networking (SDN), for optimizing resources that work toward interference.
1.3 Case Study and Performance Evaluation
Case studies and performance evaluations play a crucial role in understanding the effectiveness, challenges, and real-world applicability of various technologies and strategies in wireless communication, especially in areas such as spectrum sharing, interference mitigation, and the deployment of cooperative and cognitive radio networks [12]. These studies provide insights into practical implementation issues, performance metrics, and the potential for scalability and adaptation in different environments. Below are some illustrative examples of case studies and performance evaluations in these areas:
Spectrum Sharing in 5G Networks
Case Study: The performance evaluation in DSS between the military radars, including the incumbent apparatus to the satellite ground stations and the new generation of commercial 5G services, thereby utilizing the licensed spectrum. One such example of technology that uses environmental changes to dynamically change frequencies is called Cognitive Radio [35].
Performance Metrics: They include spectrum utilization efficiency, throughput, latency, and the impact on incumbent users. The performance evaluation presents one clear view of how DSAS can create effective access for a range of different users while protecting incumbents and gaining maximal commercial use of the band.
Interference Mitigation in Dense Urban Environments
Case Study: Advanced interference management methods are examined through intercell interference coordination (ICIC) and enhanced ICIC (eICIC) [36].
Performance Evaluation: Effectiveness through increased signal-to-interference-plus-noise ratio (SINR), enhanced user throughput, and improved overall network capacity. Results show that coordinated scheduling with power control is very promising in terms of efficient interference management in heterogeneous networks.
Cognitive Radio Networks for Rural Connectivity
Case Study: Deploy cognitive radio networks to provide broadband connectivity in rural areas by taking advantage of unused TV white spaces. They do so with the help of a best-way study of accessing the underutilized spectrum dynamically and with no interference to the incumbent broadcaster [37].
Performance Evaluation: Based on the coverage area, data rates at the end-user, accuracy of spectrum sensing, and the network’s ability to adapt to dynamic changes in the availability of the spectrum. Based on this case study, one can then paint a picture of just how cognitive radio technologies may perhaps end up bridging a certain digital divide concerning the provisioning of such services for remote and underserved areas.
Cooperative Communication for Emergency Response Networks
Case Study: Cooperative techniques in an emergency response scenario where traditional communication infrastructure does not exist or is compromised. Illustrates deploying a Mobile Ad Hoc Network (MANET) having relay nodes to facilitate communication among first responders [38].
Performance Evaluation: For the evaluation in these simulations, the performance metrics will be network connectivity, end-to-end latency, data throughput, and the robustness of the communication links considered over several states. The results have shown clear benefits for cooperative communication, which can achieve tangible gains in both higher reliability and coverage for mission-critical communication networks.
Network Slicing for IoT Applications
Case Study: One such new 5G network that includes the network slicing implementation to cover all sorts of IoT application support: from low-power wide-area networks (LPWANs) to high throughput IIoT applications [39].
Performance Evaluation: This work evaluates the performance of IoT in meeting various levels of service-level agreements (SLAs), such as low latency, high throughput, and reliability. The study brings out the efficacy of network slicing in facilitating the accommodation of heterogeneous IoT services.
Together with its attribute metrics and its simulations, they contribute to the validation not only theoretically proposed models but also, to a great extent, empirical and performance studies done in real life. They guide ongoing research efforts and development work on how to meet the requirements of the future, i.e., how wireless communication technologies will evolve.
1.4 Future Trends and Challenges
The wireless communication scenario is one replete with a very high level of dynamism and constant shift with the unabated changes in technology accompanied by an ever-expanding need for information, leading therefore to an unbridled desire for universal connectivity [14]. Against this background of change are several new trends and issues shaping the development and deployment of wireless networks. In this respect, it becomes essential to appreciate these trends if one is going to quantify the direction of both research and policies regarding development.
Implementation of AI and ML:
AI and ML are going to be the keystones in the optimization of the network functioning because of predictive maintenance or anomaly detection, dynamic spectrum management, and elimination of interference. However, they can help make real-time decisions from complex patterns and historical data, thus turning them efficient and reliable.
Technological Advancements in Spectrum Sharing:
Although the demand for wireless bandwidth is always rising, the use of available spectral bands becomes almost a compulsion. Advanced technologies like cognitive radio and dynamic spectrum access shall be further developed so that they are made even more flexible, dynamically using the shared spectrum.
Proliferation of IoT and Machine-Type Communications:
The way the IoT is coming up so fast is introducing machine-type communications that will keep growing, hence an exponential increase in connected gadgets. This will make demands on the networks with the capacity to support massive connections of many such devices themselves, each with varying and wide-ranging requirements regarding bandwidths, latencies, and power efficiencies.
The 6G and Beyond Deployment:
World communication gurus have put their effort into researching 6G technology, and it is said that a wider range of data rates, lower delays, and communications that are more trustworthy will be expected. In achieving these massive advancements, some highly potent bleeding-edge technologies are essential for 6G; these include sub-terahertz (THz) frequencies, advanced MIMO, and intelligent reflective surfaces.
Advanced Privacy-Preserving and Security Measures:
The world gets more and more dependent upon wireless communication not only from that critical infrastructural point but also from an individual device angle. As such, the network security and privacy
