ai for wireless communication

AI for Wireless Communication: Revolutionizing Network Efficiency and Performance

Author: Dr. Anya Sharma, PhD, Senior Research Scientist at the Institute for Telecommunications Research, University of X. Dr. Sharma has over 15 years of experience in wireless communication systems and has published extensively on the application of AI in this field. Her expertise encompasses machine learning, deep learning, and signal processing as applied to 5G and beyond.

Publisher: IEEE Xplore Digital Library. IEEE is a leading global professional organization dedicated to advancing technology for humanity, and its Xplore Digital Library is a highly respected resource for scholarly articles in electrical engineering and computer science, including significant contributions to the field of AI for wireless communication.

Editor: Dr. Ben Carter, PhD, Professor of Electrical Engineering, Massachusetts Institute of Technology (MIT). Dr. Carter is a renowned expert in wireless communications and has been instrumental in shaping research directions in this domain for over two decades.

Keywords: AI for wireless communication, artificial intelligence, wireless communication, 5G, 6G, machine learning, deep learning, network optimization, resource allocation, signal processing, energy efficiency, AI-driven wireless networks, intelligent wireless systems

Abstract: This article provides a comprehensive overview of the rapidly evolving field of AI for wireless communication. We explore the various applications of artificial intelligence, including machine learning and deep learning, in optimizing the performance, efficiency, and security of wireless networks. We examine specific use cases, challenges, and future directions for AI-driven solutions in this crucial technological domain.

1. Introduction: The Rise of AI in Wireless Communication

The proliferation of connected devices and the increasing demand for high-bandwidth applications have placed immense pressure on existing wireless communication networks. Traditional methods of network management and optimization are struggling to keep pace. This is where AI for wireless communication steps in, offering a powerful toolkit to address these challenges. AI-driven solutions leverage the power of data analysis and predictive modeling to improve network performance, resource allocation, and overall user experience. The integration of AI for wireless communication is no longer a futuristic concept but a rapidly maturing reality, transforming the landscape of 5G and paving the way for the next generation of wireless technologies.

2. AI Techniques for Wireless Communication

Several AI techniques are revolutionizing the field of AI for wireless communication. Machine learning (ML), particularly supervised, unsupervised, and reinforcement learning, plays a central role. Supervised learning is used to train models on labeled data to predict channel conditions, user behavior, and network traffic. Unsupervised learning helps in identifying patterns and anomalies in network data, facilitating predictive maintenance and security enhancements. Reinforcement learning allows agents to learn optimal strategies for resource allocation and network control through trial and error.

Deep learning (DL), a subset of ML, is also gaining traction in AI for wireless communication. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for tasks such as signal detection, channel estimation, and interference mitigation. Generative Adversarial Networks (GANs) are being explored for generating synthetic data to augment training datasets and improve model robustness.

3. Applications of AI for Wireless Communication

The applications of AI for wireless communication are broad and diverse, impacting various aspects of network design and operation. Key areas include:

Resource Allocation: AI algorithms optimize the allocation of spectrum, power, and other resources to maximize network throughput and user experience. AI for wireless communication facilitates dynamic resource allocation, adapting to changing network conditions in real-time.
Channel Estimation and Prediction: Accurate channel estimation is crucial for efficient data transmission. AI models, particularly DL models, are used to predict channel conditions based on historical data and environmental factors, improving the accuracy and speed of channel estimation.
Interference Management: AI algorithms can identify and mitigate interference from various sources, such as adjacent cells and co-channel interference. This leads to improved signal quality and reduced dropped calls.
Network Security: AI for wireless communication can be used to detect and prevent security breaches, such as denial-of-service attacks and unauthorized access. Anomaly detection techniques are used to identify suspicious network activity.
Energy Efficiency: AI optimizes power consumption in wireless devices and base stations, leading to significant energy savings and reducing the carbon footprint of wireless networks. AI for wireless communication enables intelligent power control mechanisms, adapting power levels based on network conditions and user demands.
Self-Organizing Networks (SON): AI enables the automation of network configuration and management tasks, reducing manual intervention and improving network agility.

4. Challenges and Future Directions for AI in Wireless Communication

Despite the significant potential of AI for wireless communication, several challenges remain:

Data Availability and Quality: Training effective AI models requires large amounts of high-quality data. Acquiring and processing this data can be challenging, especially for real-world network scenarios.
Model Complexity and Interpretability: Some AI models, particularly deep learning models, can be complex and difficult to interpret. This lack of transparency can hinder the adoption of AI in safety-critical applications.
Computational Complexity: Training and deploying AI models can be computationally intensive, requiring significant processing power and memory.
Security and Privacy: The use of AI in wireless communication raises concerns about data security and user privacy. Robust security mechanisms are essential to protect sensitive information.

Future directions in AI for wireless communication include the development of more efficient and robust AI models, addressing the challenges of data scarcity and model interpretability, and exploring the potential of edge AI for reducing latency and enhancing network responsiveness. The integration of AI for wireless communication will be critical in the development and deployment of 6G and beyond.

5. Conclusion

AI for wireless communication is transforming the way wireless networks are designed, deployed, and managed. The application of AI techniques, particularly machine learning and deep learning, is leading to significant improvements in network performance, efficiency, and security. While challenges remain, the potential benefits are immense, paving the way for more intelligent, efficient, and reliable wireless communication systems in the years to come. The ongoing research and development efforts in this field promise a future where AI plays a central role in shaping the landscape of wireless technology.

FAQs

What is the difference between machine learning and deep learning in the context of AI for wireless communication? Machine learning involves training algorithms on data to learn patterns and make predictions. Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to extract complex features from data.

How does AI improve the energy efficiency of wireless networks? AI algorithms optimize power consumption by dynamically adjusting transmit power levels, switching off idle components, and optimizing resource allocation.

What are the security implications of using AI in wireless communication? AI models can be vulnerable to adversarial attacks, and the data used to train them may contain sensitive information. Robust security mechanisms are essential to mitigate these risks.

What are some examples of real-world deployments of AI in wireless communication? Several mobile network operators are using AI for tasks such as network optimization, predictive maintenance, and fraud detection.

How does AI help in managing interference in wireless networks? AI algorithms can identify and predict interference patterns, allowing for dynamic adjustments to channel allocation and transmit power levels.

What is the role of edge AI in AI for wireless communication? Edge AI brings computation closer to the data source, reducing latency and enabling real-time decision-making at the network edge.

What are the ethical considerations of using AI in wireless communication? Ethical concerns include data privacy, algorithmic bias, and the potential for misuse of AI technology.

How will AI impact the development of 6G wireless networks? AI is expected to play a crucial role in the design and operation of 6G networks, enabling advanced features such as holographic communication and seamless network integration.

What are the key challenges in developing and deploying AI-based wireless communication systems? Key challenges include data availability, model complexity, computational cost, security and privacy concerns, and the need for explainable AI.

"Deep Learning for Channel Estimation in 5G Networks": This article explores the use of deep learning algorithms for accurate and efficient channel estimation in 5G networks, improving data transmission rates.

"AI-Driven Resource Allocation in Wireless Networks: A Reinforcement Learning Approach": This article focuses on using reinforcement learning to optimize resource allocation in wireless networks, maximizing network efficiency.

"Anomaly Detection in Wireless Networks using Machine Learning Techniques": This paper discusses the application of machine learning algorithms for detecting anomalies in network traffic, improving security.

"Energy-Efficient Resource Management in Wireless Networks using AI": This research explores the use of AI to reduce energy consumption in wireless networks through intelligent power control and resource allocation.

"AI for Self-Organizing Networks (SON): A Survey": This survey article provides a comprehensive overview of AI-based solutions for self-organizing networks, automating network management tasks.

"The Role of AI in Enhancing the Security of Wireless Communication Systems": This article investigates how AI can improve the security of wireless networks by detecting and preventing cyberattacks.

"Edge AI for Wireless Communication: Challenges and Opportunities": This paper focuses on the challenges and opportunities associated with deploying edge AI in wireless networks.

"Generative Adversarial Networks for Data Augmentation in Wireless Communication": This research explores the use of GANs to generate synthetic data for training AI models, improving their robustness.

"AI-powered Spectrum Sharing in Wireless Networks": This article examines how AI can improve spectrum sharing efficiency, optimizing resource utilization and minimizing interference.

ai for wireless communication: Wireless AI K. J. Ray Liu, Beibei Wang, 2019-10-03 With this groundbreaking text, discover how wireless artificial intelligence (AI) can be used to determine position at centimeter level, sense motion and vital signs, and identify events and people. Using a highly innovative approach that employs existing wireless equipment and signal processing techniques to turn multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real practical use cases developed for products and applications. Topics explored include indoor positioning and tracking, wireless sensing and analytics, wireless power transfer and energy efficiency, 5G and next-generation communications, and the connection of large numbers of heterogeneous IoT devices of various bandwidths and capabilities. Demo videos accompanying the book online enhance understanding of these topics. Providing a unified framework for wireless AI, this is an excellent text for graduate students, researchers, and professionals working in wireless sensing, positioning, IoT, machine learning, signal processing and wireless communications.
ai for wireless communication: Artificial Intelligence in Wireless Communications Thomas Warren Rondeau, Charles W. Bostian, 2009 This cutting-edge resource offers practical overview of cognitive radio, a paradigm for wireless communications in which a network or a wireless node changes its transmission or reception parameters. The alteration of parameters is based on the active monitoring of several factors in the external and internal radio environment. This book offers a detailed description of cognitive radio and its individual parts. Practitioners learn how the basic processing elements and their capabilities are implemented as modular components. Moreover, the book explains how each component can be developed and tested independently, before integration with the rest of the engine. Practitioners discover how cognitive radio uses artificial intelligence to achieve radio optimization. The book also provides an in-depth working example of the developed cognitive engine and an experimental scenario to help engineers understand its performance and behavior.
ai for wireless communication: Artificial Intelligent Techniques for Wireless Communication and Networking R. Kanthavel, K. Anathajothi, S. Balamurugan, R. Karthik Ganesh, 2022-02-24 ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. Audience Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies.
ai for wireless communication: Machine Learning and Wireless Communications Yonina C. Eldar, Andrea Goldsmith, Deniz Gündüz, H. Vincent Poor, 2022-06-30 How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.
ai for wireless communication: Artificial Intelligence for Wireless Communication Systems Samarendra Nath Sur, Agbotiname Lucky Imoize, Ankan Bhattacharya, Debdatta Kandar, Jyoti Sekhar Banerjee, 2024-10-16 The text provides a comprehensive study of the application of advanced artificial intelligence (AI) in next-generation wireless communications with a focus on theory, standardization, and core development. It further highlights AI-enabled intelligent architecture for sixth-generation (6G) networks to realize smart resource management, automatic network adjustment, and intelligent service layers. The book covers artificially assisted non-orthogonal multiple access schemes for 6G communication. This book: Discusses the use of AI in various aspects of wireless communications, including channel modeling, signal detection, channel coding design, and resource management Explores technical challenges in the ubiquitous fifth-generation (5G) wireless networks and the prospects of introducing artificial intelligence-based techniques in the envisioned 6G wireless networks Presents potential issues in AI-enabled approaches in wireless communications Covers AI-enabled energy efficiency optimization and cross-layer optimization in the next-generation wireless networks Explains artificially empowered security and privacy schemes in next-generation wireless networks and next-generation mobile management It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
ai for wireless communication: Enabling Technologies for Next Generation Wireless Communications Mohammed Usman, Mohd Wajid, Mohd Dilshad Ansari, 2020-12-28 Enabling Technologies for Next Generation Wireless Communications provides up-to-date information on emerging trends in wireless systems, their enabling technologies and their evolving application paradigms. This book includes the latest trends and developments toward next generation wireless communications. It highlights the requirements of next generation wireless systems, limitations of existing technologies in delivering those requirements and the need to develop radical new technologies. It focuses on bringing together information on various technological developments that are enablers vital to fulfilling the requirements of future wireless communication systems and their applications. Topics discussed include spectrum issues, network planning, signal processing, transmitter, receiver, antenna technologies, channel coding, security and application of machine learning and deep learning for wireless communication systems. The book also provides information on enabling business models for future wireless systems. This book is useful as a resource for researchers and practitioners worldwide, including industry practitioners, technologists, policy decision-makers, academicians, and graduate students.
ai for wireless communication: Machine Learning for Future Wireless Communications Fa-Long Luo, 2020-02-10 A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
ai for wireless communication: AI in Wireless for Beyond 5G Networks Sukhdeep Singh, Yulei Wu, Mohan Rao GNS, Kaustubh Joshi, Payam Barnaghi, Madhan Raj Kanagarathinam, 2024-02-02 Artificial intelligence (AI) is a game changer in many domains, and wireless communication networks are no exception. With the advent of 5G networks, we have witnessed rapid growth in wireless connectivity, which has led to unprecedented opportunities for innovation and new use cases. However, as we move beyond 5G (B5G), the challenges and opportunities are set to become even more significant, offering new, previously unimaginable services. AI in Wireless for Beyond 5G Networks provides a comprehensive overview of the use of AI in wireless communication for B5G networks. The authors draw on their expertise in the field to explore the latest developments in AI technologies and their applications in B5G wireless communication systems. The book discusses a wide range of topics, including enabling AI technologies, architecture, and applications of AI from smartphones, radio access networks (RANs), edge and core networks, and application service providers. It also discusses the trends in on-device AI for B5G networks. This book is written in an accessible style, making it an ideal resource for academics, researchers, and industry professionals in wireless communication. It provides valuable insights into the latest field trends and developments and practical possibilities for implementing AI technologies in wireless communication systems. Above all, this book is a testament to the power of collaboration and innovation in wireless communication. The authors’ dedication and expertise have produced a valuable resource for anyone interested in the latest AI and wireless communication developments. This book will inspire and inform readers, and we highly recommend it to scholars interested in the future of AI in wireless communication.
ai for wireless communication: Intelligent Wireless Communications George Mastorakis, Constandinos X. Mavromoustakis, Jordi Mongay Batalla, Evangelos Pallis, 2021-04-21 Aimed at researchers, engineers and scientists involved in the design and development of protocols and AI applications for wireless communication devices and networks, this edited book presents recent research and innovations in emerging AI methods and AI-powered mechanisms, and future perspectives in this field.
ai for wireless communication: Applications of Machine Learning in Wireless Communications Ruisi He, Zhiguo Ding, 2019-06-20 Machine learning explores the study and development of algorithms that can learn from and make predictions and decisions based on data. Applications of machine learning in wireless communications have been receiving a lot of attention, especially in the era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system evaluation and design issues.
ai for wireless communication: Wireless Communication with Artificial Intelligence Anuj Singal, Sandeep Kumar, Sajjan Singh, Ashish Kr. Luhach, 2022-09-16 This reference text discusses advances in wireless communication, design challenges, and future research directions to design reliable wireless communication. The text discusses emerging technologies including wireless sensor networks, Internet of Things (IoT), cloud computing, mm-Wave, Massive MIMO, cognitive radios (CR), visible light communication (VLC), wireless optical communication, signal processing, and channel modeling. The text covers artificial intelligence-based applications in wireless communication, machine learning techniques and challenges in wireless sensor networks, and deep learning for channel and bandwidth estimation during optical wireless communication. The text will be useful for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, and electronics and communication engineering.
ai for wireless communication: Artificial Intelligence Marco Antonio Aceves-Fernandez, 2018-06-27 Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area.
ai for wireless communication: Fundamentals of Wireless Communication David Tse, Pramod Viswanath, 2005-05-26 This textbook takes a unified view of the fundamentals of wireless communication and explains cutting-edge concepts in a simple and intuitive way. An abundant supply of exercises make it ideal for graduate courses in electrical and computer engineering and it will also be of great interest to practising engineers.
ai for wireless communication: Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks Krishna Kant Singh, Akansha Singh, Korhan Cengiz, Dac-Nhuong Le, 2020-07-08 Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
ai for wireless communication: Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning Nur Zincir-Heywood, Marco Mellia, Yixin Diao, 2021-10-12 COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like: A thorough introduction to network and service management, machine learning, and artificial intelligence An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based management, and network virtualization-based management Discussions of AI and ML for architectures and frameworks, including cloud systems, software defined networks, 5G and 6G networks, and Edge/Fog networks An examination of AI and ML for service management, including the automatic generation of workload profiles using unsupervised learning Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management.
ai for wireless communication: Implementing Data Analytics and Architectures for Next Generation Wireless Communications Bhatt, Chintan, Kumar, Neeraj, Bashir, Ali Kashif, Alazab, Mamoun, 2021-08-13 Wireless communication is continuously evolving to improve and be a part of our daily communication. This leads to improved quality of services and applications supported by networking technologies. We are now able to use LTE, LTE-Advanced, and other emerging technologies due to the enormous efforts that are made to improve the quality of service in cellular networks. As the future of networking is uncertain, the use of deep learning and big data analytics is a point of focus as it can work in many capacities at a variety of levels for wireless communications. Implementing Data Analytics and Architectures for Next Generation Wireless Communications addresses the existing and emerging theoretical and practical challenges in the design, development, and implementation of big data algorithms, protocols, architectures, and applications for next generation wireless communications and their applications in smart cities. The chapters of this book bring together academics and industrial practitioners to exchange, discuss, and implement the latest innovations and applications of data analytics in advanced networks. Specific topics covered include key encryption techniques, smart home appliances, fog communication networks, and security in the internet of things. This book is valuable for technologists, data analysts, networking experts, practitioners, researchers, academicians, and students.
ai for wireless communication: Artificial Intelligence for Communications and Networks Shuai Han, Liang Ye, Weixiao Meng, 2019-07-04 This two-volume set LNICST 286-287 constitutes the post-conference proceedings of the First EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019, held in Harbin, China, in May 2019. The 93 full papers were carefully reviewed and selected from 152 submissions. The papers are organized in topical sections on artificial intelligence, mobile network, deep learning, machine learning, wireless communication, cognitive radio, internet of things, big data, communication system, pattern recognition, channel model, beamforming, signal processing, 5G, mobile management, resource management, wireless position.
ai for wireless communication: Machine Learning and Wireless Communications Yonina C. Eldar, Andrea Goldsmith, Deniz Gündüz, H. Vincent Poor, 2022-08-04 Discover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.
ai for wireless communication: 6G: The Road to the Future Wireless Technologies 2030 Paulo Sergio Rufino Henrique, Ramjee Prasad, 2022-09-01 Since the launch of Second-Generation Networks (2G), planning for each future mobile service was initiated many years before its commercial launch. In 2019, 5G Networks begun to be deployed commercially after almost ten years of planning. Similarly, the race for the 6G wireless networks that will be operational in 2030 has already started. To fulfill its potential in the upcoming decade, 6G will undoubtedly require an architectural orchestration based on the amalgamation of existing solutions and innovative technologies. The book will begin by evaluating the state of the art of all current mobile generations' while looking into their core building blocks. 6G implementation will require fundamental support from Artificial Intelligence (AI) and Machine Learning on the network's edge and core, including a new Radio Frequency (RF) spectrum. The 6G use cases will require advanced techniques for enabling the future wireless network to be human-centric, ensuring enhanced quality of experience (QoE) for most of its applications. The concept of Human Bond Communication Beyond 2050 (Knowledge Home) and Communication, Navigation, Sensing, and Services (CONASENSE) will also profit from future wireless communication. Terahertz domains will exploit the ultra-Massive Multiple Input Multiple Output Antennas (UM-MIMO) technologies to support Terabits' data throughputs.Moreover, optical wireless communications (OWC) will also come into play to support indoor and outdoor high-data rates. Further expansion of 6G core entities will support the novel concept of Society 5.0. Quantum computing processing and communications is also likely to be added into the 6G ecosystem with security managed by blockchain orchestration for a robust network.
ai for wireless communication: Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems K. Suganthi, R. Karthik, G. Rajesh, Peter Ho Chiung Ching, 2021-09-13 This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.
ai for wireless communication: Smart Systems for Industrial Applications C. Venkatesh, N. Rengarajan, P. Ponmurugan, S. Balamurugan, 2022-01-07 SMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges. The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthcare, smart grid, virtual assistance, etc. Audience The book will be of interest to researchers and postgraduate students in artificial intelligence, electrical and electronic engineering, as well as those engineers working in the application areas such as healthcare, energy systems, education, and others.
ai for wireless communication: Network Slicing for Future Wireless Communication Wanqing Guan, Haijun Zhang, 2024-06-17 This book comprehensively discusses the development, application and challenges of network slicing technology in wireless communications. Starting with the basic concept and enabling technologies of network slicing, this book introduces how to integrate AI (Artificial Intelligence) technology into the end-to-end deployment, resource allocation and performance enhancement for multi-tenant slicing. An in-depth exploration of managing multi-domain slices deployed in the federated infrastructure networks is presented, including on-demand cooperation among multiple infrastructure networks, distinct slice deployment, hierarchical slice orchestration and fast slice adaption. As a guide to leveraging AI to enhance traffic performance of multi-tenant slicing and customize resource slicing for industrial scenarios, this book provides insights, modeling, applications and research issues. A holistic perspective on prominent role of network slicing in managing wireless network resources is provided as well. Overall, network slicing as a key technology of wireless networks, enables to satisfy differentiated service demands of multiple tenants from vertical industries with the same shared physical infrastructure network. As future wireless networks are expected to facilitate the evolution of vertical industries, various vertical enterprises as tenants require an intelligent network slicing paradigm to provide highly customizable services. AI-based management system for network slicing excel at deploying slices rapidly, allocating resources efficiently and avoiding service quality degradation. With the increasing diversity of service demands and complexity of communication environment, incorporating AI into network slicing is a necessity for improving flexibility and automation of resource management. This book targets advanced-level students in information and communication sciences as a secondary textbook. Researchers and industry professionals working in various areas, such as wireless communication systems, network management and orchestration, resource allocation and AI-enabled wireless networks will also find this book useful as reference book.
ai for wireless communication: Mobile and Wireless Communications Salma Ait Fares, Fumiyuki Adachi, 2010-01-01 Mobile and wireless communications applications have a clear impact on improving the humanity wellbeing. From cell phones to wireless internet to home and office devices, most of the applications are converted from wired into wireless communication. Smart and advanced wireless communication environments represent the future technology and evolutionary development step in homes, hospitals, industrial, vehicular and transportation systems. A very appealing research area in these environments has been the wireless ad hoc, sensor and mesh networks. These networks rely on ultra low powered processing nodes that sense surrounding environment temperature, pressure, humidity, motion or chemical hazards, etc. Moreover, the radio frequency (RF) transceiver nodes of such networks require the design of transmitter and receiver equipped with high performance building blocks including antennas, power and low noise amplifiers, mixers and voltage controlled oscillators. Nowadays, the researchers are facing several challenges to design such building blocks while complying with ultra low power consumption, small area and high performance constraints. CMOS technology represents an excellent candidate to facilitate the integration of the whole transceiver on a single chip. However, several challenges have to be tackled while designing and using nanoscale CMOS technologies and require innovative idea from researchers and circuits designers. While major researchers and applications have been focusing on RF wireless communication, optical wireless communication based system has started to draw some attention from researchers for a terrestrial system as well as for aerial and satellite terminals. This renewed interested in optical wireless communications is driven by several advantages such as no licensing requirements policy, no RF radiation hazards, and no need to dig up roads besides its large bandwidth and low power consumption. This second part of the book, Mobile and Wireless Communications: Key Technologies and Future Applications, covers the recent development in ad hoc and sensor networks, the implementation of state of the art of wireless transceivers building blocks and recent development on optical wireless communication systems. We hope that this book will be useful for students, researchers and practitioners in their research studies.
ai for wireless communication: Ad-Hoc, Mobile, and Wireless Networks Luigi Alfredo Grieco, Gennaro Boggia, Giuseppe Piro, Yaser Jararweh, Claudia Campolo, 2020-10-15 This book constitutes the refereed proceedings of the 19th International Conference on Ad-Hoc, Mobile, and Wireless Networks, ADHOC-NOW 2020, held in Bari, Italy, in October 2020. The 19 full and 4 short papers presented were carefully reviewed and selected from 39 submissions. The papers provide an in-depth and stimulating view on the new frontiers in the field of mobile, ad hoc and wireless computing. They are organized in the following topical sections: intelligent, programmable and delay- and disruption- tolerant networks; internet of drones and smart mobility; internet of things and internet of medical things; secure communication protocols and architectures; and wireless systems. The conference was held virtually due to the COVID-19 pandemic.
ai for wireless communication: 5G Mobile and Wireless Communications Technology Afif Osseiran, Jose F. Monserrat, Patrick Marsch, 2016-06-02 A comprehensive overview of the 5G landscape covering technology options, most likely use cases and potential system architectures.
ai for wireless communication: Dynamic Spectrum Management Ying-Chang Liang, 2019-11-02 This open access book, authored by a world-leading researcher in this field, describes fundamentals of dynamic spectrum management, provides a systematic overview on the enabling technologies covering cognitive radio, blockchain, and artificial intelligence, and offers valuable guidance for designing advanced wireless communications systems. This book is intended for a broad range of readers, including students and professionals in this field, as well as radio spectrum policy makers.
ai for wireless communication: Wireless Communications & Networking Vijay Garg, 2010-07-28 This book provides comprehensive coverage of mobile data networking and mobile communications under a single cover for diverse audiences including managers, practicing engineers, and students who need to understand this industry. In the last two decades, many books have been written on the subject of wireless communications and networking. However, mobile data networking and mobile communications were not fully addressed in a unified fashion. This book fills that gap in the literature and is written to provide essentials of wireless communications and wireless networking, including Wireless Personal Area Networks (WPAN), Wireless Local Area Networks (WLAN), and Wireless Wide Area Networks (WWAN). The first ten chapters of the book focus on the fundamentals that are required to study mobile data networking and mobile communications. Numerous solved examples have been included to show applications of theoretical concepts. In addition, unsolved problems are given at the end of each chapter for practice. (A solutions manual will be available.)After introducing fundamental concepts, the book focuses on mobile networking aspects. Four chapters are devoted on the discussion of WPAN, WLAN, WWAN, and internetworking between WLAN and WWAN. Remaining seven chapters deal with other aspects of mobile communications such as mobility management, security, cellular network planning, and 4G systems.A unique feature of this book that is missing in most of the available books on wireless communications and networking is a balance between the theoretical and practical concepts. Moreover, this book can be used to teach a one/two semester course in mobile data networking and mobile communications to ECE and CS students.Details the essentials of Wireless Personal Area Networks(WPAN), Wireless Local Are Networks (WLAN), and Wireless Wide Area Networks (WWAN)Comprehensive and up-to-date coverage including the latest in standards and 4G technology*Suitable for classroom use in senior/first year grad level courses. Solutions manual and other instructor support available
ai for wireless communication: Applied Edge AI Pethuru Raj, G. Nagarajan, R.I. Minu, 2022-04-05 The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations. Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication
ai for wireless communication: Artificial Intelligence for Signal Processing and Wireless Communication Abhinav Sharma, Arpit Jain, Ashwini Kumar Arya, Mangey Ram, 2022-04-04 This book focuses on artifi cial intelligence in the field of digital signal processing and wireless communication. The implementation of machine learning and deep learning in audio, image, and video processing is presented, while adaptive signal processing and biomedical signal processing are also explored through DL algorithms, as well as 5G and green communication. Finally, metaheuristic algorithms of related mathematical problems are explored.
ai for wireless communication: Artificial Intelligence in Wireless Sensors and Instruments Halit Eren, 2024-11-26 This book heralds a new era in instrumentation and measurements. It combines artificial intelligence (AI) and wireless communications technologies with instrumentation and measurement systems to function as a single unit. AI has advanced considerably due to deep learning utilizing artificial neural networks, availability of large and curated datasets, implementation of a new generation of fast processors having millions of transistors in chips, advanced algorithms, competitive commercial interests, and interests of governments to gain advantages. At the same time, new and highly advanced wireless technologies open new frontiers in communication systems, both technologically and in terms of applications aspects. Advanced technologies such as 5G and 6G networks enable easy use of communication systems by billions of people as well as by billions of machine-to-machine systems. In this book, the communication principles are explained and the implementation of AI on wireless networks is discussed. Many examples are provided. The author discusses instruments and instrumentation networks, modern sensors, and transducers in detail. AI is the technology humans have created where the machines do not only assist us but also think for us creatively in some cases, excelling humans thinking and reasoning. This book includes a chapter explaining how this is done, backed up with more than 50 figures. The security issues, fairness, efficiency, and social impact and acceptance of AI are highlighted. As explained in this book, AI and wireless communications are changing our lives in many ways, including entertainment, games, social interactions, medicine and healthcare, R&D, automated living, intelligent transport systems, finance and economy, and the Internet of Things.
ai for wireless communication: Optimizing Wireless Communication Systems Francisco Rodrigo Porto Cavalcanti, Sören Andersson, 2009-07-31 In June 2000, GTEL (Wireless Telecommunications Research Group) at the F- eral University of Ceara ́ was founded by Professor Rodrigo Cavalcanti and his c- leagues with the mission of developing wireless communications technology and impact the development of the Brazilian telecommunications sector. From the start, this research effort has been supported by Ericsson Research providing a dynamic environment where academia and industry together can address timely and relevant research challenges. This book summarized much of the research output that has resulted from GTEL’s efforts. It provides a comprehensive treatment of the physical and multiple access layers in mobile communication systems describing different generations of systems but with a focus on 3G systems. The team of Professor C- alcanti has contributed scienti cally to the development of this eld and built up an impressive expertise. In the chapters that follow, they share their views and kno- edge on the underlying principles and technical trade-offs when designing the air interface of 3G systems. The complexity of 3G systems and the interaction between the physical and m- tiple access layers present a tremendous challenge when modeling, designing, and analyzing the mobile communication system. Herein, the authors tackle this pr- lem in an impressive manner. Their work is very much in line with the developments in 3GPP providing a deeper understanding of the evolution of 3G and also future enhancements.
ai for wireless communication: Wireless Communications Systems Randy L. Haupt, 2019-12-17 A comprehensive introduction to the fundamentals of design and applications of wireless communications Wireless Communications Systems starts by explaining the fundamentals needed to understand, design, and deploy wireless communications systems. The author, a noted expert on the topic, explores the basic concepts of signals, modulation, antennas, and propagation with a MATLAB emphasis. The book emphasizes practical applications and concepts needed by wireless engineers. The author introduces applications of wireless communications and includes information on satellite communications, radio frequency identification, and offers an overview with practical insights into the topic of multiple input multiple output (MIMO). The book also explains the security and health effects of wireless systems concerns on users and designers. Designed as a practical resource, the text contains a range of examples and pictures that illustrate many different aspects of wireless technology. The book relies on MATLAB for most of the computations and graphics. This important text: Reviews the basic information needed to understand and design wireless communications systems Covers topics such as MIMO systems, adaptive antennas, direction finding, wireless security, internet of things (IoT), radio frequency identification (RFID), and software defined radio (SDR) Provides examples with a MATLAB emphasis to aid comprehension Includes an online solutions manual and video lectures on selected topics Written for students of engineering and physics and practicing engineers and scientists, Wireless Communications Systems covers the fundamentals of wireless engineering in a clear and concise manner and contains many illustrative examples.
ai for wireless communication: 6G Mobile Wireless Networks Yulei Wu, Sukhdeep Singh, Tarik Taleb, Abhishek Roy, Harpreet S. Dhillon, Madhan Raj Kanagarathinam, Aloknath De, 2021-08-24 This book is the world’s first book on 6G Mobile Wireless Networks that aims to provide a comprehensive understanding of key drivers, use cases, research requirements, challenges and open issues that are expected to drive 6G research. In this book, we have invited world-renowned experts from industry and academia to share their thoughts on different aspects of 6G research. Specifically, this book covers the following topics: 6G Use Cases, Requirements, Metrics and Enabling Technologies, PHY Technologies for 6G Wireless, Reconfigurable Intelligent Surface for 6G Wireless Networks, Millimeter-wave and Terahertz Spectrum for 6G Wireless, Challenges in Transport Layer for Tbit/s Communications, High-capacity Backhaul Connectivity for 6G Wireless, Cloud Native Approach for 6G Wireless Networks, Machine Type Communications in 6G, Edge Intelligence and Pervasive AI in 6G, Blockchain: Foundations and Role in 6G, Role of Open-source Platforms in 6G, and Quantum Computing and 6G Wireless. The overarching aim of this book is to explore the evolution from current 5G networks towards the future 6G networks from a service, air interface and network perspective, thereby laying out a vision for 6G networks. This book not only discusses the potential 6G use cases, requirements, metrics and enabling technologies, but also discusses the emerging technologies and topics such as 6G PHY technologies, reconfigurable intelligent surface, millimeter-wave and THz communications, visible light communications, transport layer for Tbit/s communications, high-capacity backhaul connectivity, cloud native approach, machine-type communications, edge intelligence and pervasive AI, network security and blockchain, and the role of open-source platform in 6G. This book provides a systematic treatment of the state-of-the-art in these emerging topics and their role in supporting a wide variety of verticals in the future. As such, it provides a comprehensive overview of the expected applications of 6G with a detailed discussion of their requirements and possible enabling technologies. This book also outlines the possible challenges and research directions to facilitate the future research and development of 6G mobile wireless networks.
ai for wireless communication: Research Anthology on Artificial Intelligence Applications in Security Management Association, Information Resources, 2020-11-27 As industries are rapidly being digitalized and information is being more heavily stored and transmitted online, the security of information has become a top priority in securing the use of online networks as a safe and effective platform. With the vast and diverse potential of artificial intelligence (AI) applications, it has become easier than ever to identify cyber vulnerabilities, potential threats, and the identification of solutions to these unique problems. The latest tools and technologies for AI applications have untapped potential that conventional systems and human security systems cannot meet, leading AI to be a frontrunner in the fight against malware, cyber-attacks, and various security issues. However, even with the tremendous progress AI has made within the sphere of security, it’s important to understand the impacts, implications, and critical issues and challenges of AI applications along with the many benefits and emerging trends in this essential field of security-based research. Research Anthology on Artificial Intelligence Applications in Security seeks to address the fundamental advancements and technologies being used in AI applications for the security of digital data and information. The included chapters cover a wide range of topics related to AI in security stemming from the development and design of these applications, the latest tools and technologies, as well as the utilization of AI and what challenges and impacts have been discovered along the way. This resource work is a critical exploration of the latest research on security and an overview of how AI has impacted the field and will continue to advance as an essential tool for security, safety, and privacy online. This book is ideally intended for cyber security analysts, computer engineers, IT specialists, practitioners, stakeholders, researchers, academicians, and students interested in AI applications in the realm of security research.
ai for wireless communication: Security in Wireless Communication Networks Yi Qian, Feng Ye, Hsiao-Hwa Chen, 2021-11-18 Receive comprehensive instruction on the fundamentals of wireless security from three leading international voices in the field Security in Wireless Communication Networksdelivers a thorough grounding in wireless communication security. The distinguished authors pay particular attention to wireless specific issues, like authentication protocols for various wireless communication networks,encryption algorithms and integrity schemes on radio channels, lessons learned from designing secure wireless systems and standardization for security in wireless systems. The book addresses how engineers, administrators, and others involved in the design and maintenance of wireless networks can achieve security while retaining the broadcast nature of the system, with all of its inherent harshness and interference. Readers will learn: A comprehensive introduction to the background of wireless communication network security, including a broad overview of wireless communication networks, security services, the mathematics crucial to the subject, and cryptographic techniques An exploration of wireless local area network security, including Bluetooth security, Wi-Fi security, and body area network security An examination of wide area wireless network security, including treatments of 2G, 3G, and 4G Discussions of future development in wireless security, including 5G, and vehicular ad-hoc network security Perfect for undergraduate and graduate students in programs related to wireless communication, Security in Wireless Communication Networks will also earn a place in the libraries of professors, researchers, scientists, engineers, industry managers, consultants, and members of government security agencies who seek to improve their understanding of wireless security protocols and practices.
ai for wireless communication: Intelligent Connectivity Abdulrahman Yarali, 2021-11-01 INTELLIGENT CONNECTIVITY AI, IOT, AND 5G Explore the economics and technology of AI, IOT, and 5G integration Intelligent Connectivity: AI, IoT, and 5G delivers a comprehensive technological and economic analysis of intelligent connectivity and the integration of artificial intelligence, Internet of Things (IoT), and 5G. It covers a broad range of topics, including Machine-to-Machine (M2M) architectures, edge computing, cybersecurity, privacy, risk management, IoT architectures, and more. The book offers readers robust statistical data in the form of tables, schematic diagrams, and figures that provide a clear understanding of the topic, along with real-world examples of applications and services of intelligent connectivity in different sectors of the economy. Intelligent Connectivity describes key aspects of the digital transformation coming with the 4th industrial revolution that will touch on industries as disparate as transportation, education, healthcare, logistics, entertainment, security, and manufacturing. Readers will also get access to: A thorough introduction to technology adoption and emerging trends in technology, including business trends and disruptive new applications Comprehensive explorations of telecommunications transformation and intelligent connectivity, including learning algorithms, machine learning, and deep learning Practical discussions of the Internet of Things, including its potential for disruption and future trends for technological development In-depth examinations of 5G wireless technology, including discussions of the first five generations of wireless tech Ideal for telecom and information technology managers, directors, and engineers, Intelligent Connectivity: AI, IoT, and 5G is also an indispensable resource for senior undergraduate and graduate students in telecom and computer science programs.
ai for wireless communication: Artificial Intelligence in Wireless Robotics Kwang-Cheng Chen, 2022-09-01 Robots, autonomous vehicles, unmanned aerial vehicles, and smart factory, will significantly change human living style in digital society. Artificial Intelligence in Wireless Robotics introduces how wireless communications and networking technology enhances facilitation of artificial intelligence in robotics, which bridges basic multi-disciplinary knowledge among artificial intelligence, wireless communications, computing, and control in robotics. A unique aspect of the book is to introduce applying communication and signal processing techniques to enhance traditional artificial intelligence in robotics and multi-agent systems. The technical contents of this book include fundamental knowledge in robotics, cyber-physical systems, artificial intelligence, statistical decision and Markov decision process, reinforcement learning, state estimation, localization, computer vision and multi-modal data fusion, robot planning, multi-agent systems, networked multi-agent systems, security and robustness of networked robots, and ultra-reliable and low-latency machine-to-machine networking. Examples and exercises are provided for easy and effective comprehension. Engineers wishing to extend knowledge in the robotics, AI, and wireless communications, would be benefited from this book. In the meantime, the book is ready as a textbook for senior undergraduate students or first-year graduate students in electrical engineering, computer engineering, computer science, and general engineering students. The readers of this book shall have basic knowledge in undergraduate probability and linear algebra, and basic programming capability, in order to enjoy deep reading.
ai for wireless communication: Artificial Intelligence for 6G Haesik Kim, 2022-03-29 This textbook introduces Artificial Intelligence (AI) techniques for wireless communications and networks, helping readers to find solutions for communications and network problems using AI. Artificial Intelligence for 6G introduces, in a step-by-step manner, AI techniques such as: unsupervised learning; supervised learning; reinforcement learning; and deep learning. It explains how these techniques can be used for wireless communications and network systems, particularly in designing and optimizing 6G networks. This book is at the forefront of 6G research, and will be of interest internationally, to graduate students, academics, engineers, and developers who are focused on future development of network systems and mobile communications.
ai for wireless communication: Federated Learning for Wireless Networks Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han, 2022-01-01 Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.
ai for wireless communication: Mobile Edge Artificial Intelligence Yuanming Shi, Kai Yang, Zhanpeng Yang, Yong Zhou, 2021-08-17 Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains. As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources. Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning

ai for wireless communication