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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.
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