The conference focus on the general area of signal processing advances in wireless communications It will include, but not limited to, the following topics Coding, Modulation, and Equalization Detection, Estimation and Filtering in Wireless Communications Beamforming, Precoding and Transceiver Designs of MIMO Massive and millimeter MIMO systems Full duplex Communications Energy Efficiency and Energy Harvesting Network Design and Performance Optimization Cooperative Communications and Multiuser Networks Resource Allocation and Scheduling in Wireless Networks Internet of Things and Connected Vehicles Distributed Signal Processing Localization and Tracking in Wireless Networks Machine Learning for Wireless Communications and Networking Future Trends and Emerging Technologies
This book is a collection of research articles presented at the 4th International Conference on Communications and Cyber-Physical Engineering (ICCCE 2021), held on April 9 and 10, 2021, at CMR Engineering College, Hyderabad, India. ICCCE is one of the most prestigious conferences conceptualized in the field of networking and communication technology offering in-depth information on the latest developments in voice, data, image, and multimedia. Discussing the latest developments in voice and data communication engineering, cyber-physical systems, network science, communication software, image, and multimedia processing research and applications, as well as communication technologies and other related technologies, it includes contributions from both academia and industry. This book is a valuable resource for scientists, research scholars, and PG students working to formulate their research ideas and find the future directions in these areas. Further, it may serve as a reference work to understand the latest engineering and technologies used by practicing engineers in the field of communication engineering.
This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well. The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems. The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included. This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.
The coming generations of wireless network technologies will serve, not only as a means of connecting physical and digital environments, but also to set the foundation for an intelligent world in which all aspects are interconnected, sensed, and endowed with intelligence. Beyond merely providing communication capabilities, future networks will have the capacity to "see" and interpret the physical world. This development compels us to re-imagine the design of current communication infrastructures and terminals, taking into account crucial aspects such as fundamental constraints and tradeoffs, information extraction and processing technologies, issues of public security and privacy, as well as the emergence of numerous new applications. This field of research is known as Integrated Sensing and Communications (ISAC), and it has ushered in a paradigm shift towards the omnipresence of radio devices.This book provides the first comprehensive introduction to the ISAC theoretical and practical framework. Each chapter is authored by a group of world-leading experts, including over 10 IEEE Fellows. Readers can expect to gain both a broad overview and detailed technical insights into the latest ISAC innovations.
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.
This book gathers selected high-quality research papers presented at the Eighth International Congress on Information and Communication Technology, held at Brunel University, London, on 20–23 February 2023. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT) and e-mining. Written by respected experts and researchers working on ICT, the book offers a valuable asset for young researchers involved in advanced studies. The work is presented in four volumes.
This book constitutes the refereed proceedings of the 15th International Conference on Flexible Query Answering Systems, FQAS 2023, held in September 2023 in Palma de Mallorca, Spain. The 24 full papers presented were carefully reviewed and selected from numerous submissions. They are organized in the following topical sections: Flexible Queries over Semantic Systems; Advanced Methods and Applications in Natural Language; Processing (NLP); New Advances in Disinformation Detection; Data and Text Mining; Applying AI to Social Science and Social Science to AI; Artificial Intelligence Law and Regulation.
Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance. In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems. - Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systems - Highlights the need to design efficient federated learning-based algorithms to tackle the proliferating security and patient privacy issues in digital healthcare systems - Reviews the latest research, along with practical solutions and applications developed by global experts from academia and industry
