Ultra-Reliable and Low-Latency Communications (URLLC) Theory and Practice Comprehensive resource presenting important recent advances in wireless communications for URLLC services, including device-to-device communication, multi-connectivity, and more Ultra-Reliable and Low-Latency Communications (URLLC) Theory and Practice discusses the typical scenarios, possible solutions, and state-of-the-art techniques that enable URLLC in different perspectives from the physical layer to higher-level approaches, aiming to tackle URLLC’s challenges with both theoretical and practical approaches, which bridges the lacuna between theory and practice. With long-term contributions to the development of future wireless networks, the text systematically presents a thorough study of the novel and innovative paradigm of URLLC; basic requirements are covered, along with essential definitions, state-of-the-art technologies, and promising research directions of URLLC. To aid in reader comprehension, tables, figures, design schematics, and examples are provided to illustrate abstract engineering concepts and make the text more accessible to a broader readership, and corresponding case studies are included in the last part of the book. Fundamental problems in URLLC, including designing building blocks for URLLC, radio resource management in URLLC, resource optimization, network availability guarantee, and coexisting with other future mobile networks, are also discussed. In Ultra-Reliable and Low-Latency Communications (URLLC) Theory and Practice, readers can expect to find detailed information on: BCH and analog codes, stable matching, OFDM demodulation and turbo coding, and semi-blind receivers for URLLC MIMO-NOMA with URLLC, PHY and MAC layer technologies for URLLC, and Network slicing or SDN for URLLC and eMBB Integrating theoretical knowledge into deep learning for URLLC, Energy-Latency tradeoff in URLLC, and Downlink transmission for URLLC under physical layer aspects Resource allocation for multi-user downlink URLLC, HARQ optimization for 5G URLLC, and Multi-Access edge computing with URLLC A unique resource with comprehensive yet accessible coverage of a complicated subject, Ultra-Reliable and Low-Latency Communications (URLLC) Theory and Practice is an ideal resource for a large and diverse population of researchers and practitioners in engineering, computer scientists, and senior undergraduate and graduate students in related programs of study.

A thorough treatment of UAV wireless communications and networking research challenges and opportunities. Detailed, step-by-step development of carefully selected research problems that pertain to UAV network performance analysis and optimization, physical layer design, trajectory path planning, resource management, multiple access, cooperative communications, standardization, control, and security is provided. Featuring discussion of practical applications including drone delivery systems, public safety, IoT, virtual reality, and smart cities, this is an essential tool for researchers, students, and engineers interested in broadening their knowledge of the deployment and operation of communication systems that integrate or rely on unmanned aerial vehicles.

The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective. Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more. This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems. Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.