Signal Processing - January 2017 - 111

feature supported by next-generation cellular networks. This book introduces
D2D-based wireless communications
from the physical-, media access control-,
network-, and application-layer perspectives, providing all the key background
information before moving on to discuss
real-world applications as well as potential
future developments. Key topics are discussed in detail, such as dynamic resource
sharing (e.g., of spectrum and power)
between cellular and ad hoc D2D communications to accommodate larger volumes
of traffic and provide better service to
users. Readers will understand the practical challenges of resource management,
optimization, security, standardization,
and network topology, and learn how the
design principles are applied in practice.

Shuguang Cui, Alfred O. Hero III, Zhi-quan
Luo, and José M.F. Moura (Editors). Big
Data over Networks. Cambridge University
Press, Year 2016, ISBN: 9781107099005.
Utilizing both key
mathematical tools
and state-of-the-art
research results, this
text explores the
principles underpinning large-scale
information processing over networks and examines the
crucial interaction between big data and
its associated communication, social, and
biological networks.
Written by experts in the diverse fields
of machine learning, optimization, statistics, signal processing, networking, communications, sociology, and biology, this
book employs two complementary
approaches: 1) analyzing how the underlying network constrains the upper layer
of collaborative big data processing and
2) examining how big data processing
may boost performance in various networks. Unifying the broad scope of the
book is the rigorous mathematical treatment of the subjects, which is enriched by
in-depth discussion of future directions
and numerous open-ended problems that
conclude each chapter. Readers will be
able to master the fundamental principles
for dealing with big data over large systems, making it essential reading for grad-

uate students, scientific researchers, and
industry practitioners alike.

Guowang Miao, Jens Zander, Ki Won
Sung, and Slimane Ben Slimane.
Fundamentals of Mobile Data Networks.
Cambridge University Press, Year: 2016,
ISBN: 9781107143210.
This unique text
provides a comprehensive and systematic introduction to
the theory and practice of mobile data
networks. Covering
basic design principles as well as analytical tools for network
performance evaluation, and with a focus
on system-level resource management,
you will learn how state-of-the-art network design can enable you to flexibly
and efficiently manage and trade off various resources such as spectrum, energy,
and infrastructure investments. Topics
covered range from traditional elements
such as medium access, cell deployment,
capacity, handover, and interference management, to more recent cutting-edge
topics such as heterogeneous networks,
energy and cost-efficient network design,
and a detailed introduction to long-term
evolution (4G). Numerous worked examples and exercises illustrate the key theoretical concepts and help you put your
knowledge into practice, making this an
essential resource whether you are a student, researcher, or practicing engineer.

Albert-László Barbási. Network Science.
Cambridge University Press, Year: 2016,
ISBN: 9781107076266.
Networks are everywhere, from the
In ternet, to social
networks, and the
genetic networks that
determine our biological existence. Illustrated throughout in
full color, this pioneering textbook, spanning
a wide range of topics from physics to computer science, engineering, economics and
social sciences, introduces network science
to an interdisciplinary audience.
IEEE SIGNAL PROCESSING MAGAZINE

January 2017

From the origins of the six degrees of
separation to explaining why networks are
robust to failures and fragile to attacks,
the author explores how viruses like
Ebola and H1N1 spread and why it is that
our friends have more friends than we do.
Using numerous real-world examples,
this innovative text includes clear delineation between undergraduate- and graduate-level material. The mathematical
formulas and derivations are included
within advanced topics sections, enabling
use at a range of levels. Extensive online
resources, including films and software
for network analysis, make this a multifaceted companion for anyone with an
interest in network science.

Vikram Krishnamurthy. Partially Observed
Markov Decision Processes: From Filtering
to Controlled Sensing. Cambridge
University Press, Year: 2016,
ISBN: 9781316471104.
Covering formulation, algorithms,
and structural results and linking
theory to real-world
applications in controlled sensing
(including social
learning, adaptive radars, and sequential
detection), this book focuses on the conceptual foundations of partially observable Markov decision processes
(POMPDs). It emphasizes structural
results in stochastic dynamic programming, enabling graduate students and
researchers in engineering, operations
research, and economics to understand
the underlying unifying themes without
getting weighed down by mathematical
technicalities. Bringing together research
from across the literature, the book provides an introduction to nonlinear filtering
followed by a systematic development of
stochastic dynamic programming, lattice
programming, and reinforcement learning
for POMDPs.
The abstraction of POMDPs becomes
alive with applications. This book contains several examples starting from target
tracking in Bayesian filtering to optimal
search, risk measures, active sensing,
adaptive radars, and social learning.
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Table of Contents for the Digital Edition of Signal Processing - January 2017