|
Showing 1 - 3 of
3 matches in All Departments
Deep Reinforcement Learning for Wireless Communications and
Networking Comprehensive guide to Deep Reinforcement Learning (DRL)
as applied to wireless communication systems Deep Reinforcement
Learning for Wireless Communications and Networking presents an
overview of the development of DRL while providing fundamental
knowledge about theories, formulation, design, learning models,
algorithms and implementation of DRL together with a particular
case study to practice. The book also covers diverse applications
of DRL to address various problems in wireless networks, such as
caching, offloading, resource sharing, and security. The authors
discuss open issues by introducing some advanced DRL approaches to
address emerging issues in wireless communications and networking.
Covering new advanced models of DRL, e.g., deep dueling
architecture and generative adversarial networks, as well as
emerging problems considered in wireless networks, e.g., ambient
backscatter communication, intelligent reflecting surfaces and edge
intelligence, this is the first comprehensive book studying
applications of DRL for wireless networks that presents the
state-of-the-art research in architecture, protocol, and
application design. Deep Reinforcement Learning for Wireless
Communications and Networking covers specific topics such as: Deep
reinforcement learning models, covering deep learning, deep
reinforcement learning, and models of deep reinforcement learning
Physical layer applications covering signal detection, decoding,
and beamforming, power and rate control, and physical-layer
security Medium access control (MAC) layer applications, covering
resource allocation, channel access, and user/cell association
Network layer applications, covering traffic routing, network
classification, and network slicing With comprehensive coverage of
an exciting and noteworthy new technology, Deep Reinforcement
Learning for Wireless Communications and Networking is an essential
learning resource for researchers and communications engineers,
along with developers and entrepreneurs in autonomous systems, who
wish to harness this technology in practical applications.
The latest advances in several emerging technologies such as AI,
blockchain, privacy-preserving algorithms used in localization and
positioning systems, cloud computing and computer vision all have
great potential in facilitating social distancing. Benefits range
from supporting people to work from home to monitoring micro- and
macro- movements such as contact tracing apps using Bluetooth,
tracking the movement and transportation level of a city and
wireless positioning systems to help people keep a safe distance by
alerting them when they are too close to each other or to avoid
congestion. However, implementing such technologies in practical
scenarios still faces various challenges. This book aims to lay the
foundations of how these technologies could be adopted to realize
and facilitate social distancing to better manage pandemics and
future outbreaks. Starting with basic concepts, models and
practical technology-based social distancing scenarios, the authors
present enabling wireless technologies and solutions which could be
widely adopted to encourage social distancing. They include symptom
prediction, detection and monitoring of quarantined people and
contact tracing. In the future, smart infrastructures for
next-generation wireless systems should incorporate a pandemic mode
in their standard architecture and design.
|
|