Signal Processing - January 2017 - 112

The supplement of this book contains
errata and problem sets and can be
accessed via: http://www.cambridge
.org/gb/academic/subjects/engineering/
communications-and-signal-processing/
partially-observed-markov-decisionprocesses-filtering-controlled-sensing?
format=HB.

Fa-Long Luo and Charlie Jianzhong Zhang
(Editors). Signal Processing for 5G:
Algorithms and Implementations.
Wiley-IEEE Press, Year: 2016,
ISBN: 9781119116462.
Signal processing techniques have played
the most important role in wireless

LECTURE NOTES

This book is a comprehensive and
detailed guide to all signal processing techniques employed in 5G wireless networks.
Uniquely organized into four categories,
"New Modulation and Coding, "New Spatial Processing," "New Spectrum Opportunities," and "New System-Level Enabling
Technologies," it covers everything from
network architecture, physical layer
(down-link and up-link), protocols and air
interface, to cell acquisition, scheduling
and rate adaption, access procedures, and
relaying to spectrum allocations. All technology aspects and major roadmaps of
global 5G standard development and
deployments are included in the book.
SP

(continued from page 103)

Conclusions
This article introduces a new paradigm
of PP techniques-CP-which represents a dimension-reduced subspace
approach to PP machine learning. Built
upon the information and estimation
theory, CP methods tackle joint optimization over feature/utility/privacy spaces.
This leads to several eigen-system-based
subspace methods, including PCA,
DCA, and DUCA. To confirm the theoretical analysis, we have conducted
experimental studies on various DIP and
FR problems. The latter also demonstrates possible real-world applications
of the proposed CP methodology.

Acknowledgments
This material is based upon work supported in part by the Brandeis Program
of DARPA and the Space and U.S.
Naval Warfare System Center Pacific
(SSC Pacific) under contract 66001-15C-4068. I would like to thank Prof.
Morris J. Chang (ISU) and Prof. Peiyuan
112

communications
since the second
generation of cellular systems. It is
anticipated that
new techniques
employed in fifthgeneration (5G)
wireless networks will not only improve
peak service rates significantly but also
enhance capacity, coverage, reliability,
low-latency, efficiency, flexibility, compatibility, and convergence to meet the
increasing demands imposed by applications such as big data, cloud service,
machine-to-machine, and mission-critical communications.

Wu, Yuan Zhou, Ying Li, Dr. Shibiao
Wan, Thee Chanyaswad, Mert Al, Chang
Chang Liu, and Artur Filipowicz
(Princeton University) for invaluable discussions and assistance.

Author
S.Y. Kung (kung@princeton.edu) is a
professor in the Department of Electrical
Engineering at Princeton University, New
Jersey. His research areas include
machine learning, compressive privacy,
data mining and analysis, statistical estimation, system identification, wireless
communication, very-large-scale integration (VLSI) array processors, genomic
signal processing, and multimedia information processing. He was a founding
member of several technical committees
of the IEEE Signal Processing Society. He
served as a member of the Board of
Governors of the IEEE Signal Processing
Society (1989-1991). He has been the
editor-in-chief of Journal of VLSI Signal
Processing Systems since 1990. He has
IEEE SIGNAL PROCESSING MAGAZINE

January 2017

received multiple awards and recognitions. He is a Life Fellow of the IEEE.

References

[1] C. Dwork, K. Kenthapadi,F, McSherry, I. Mironov,
and M. Naor, "Our data, ourselves: Privacy via distributed noise generation," in Advancves in CryptologyEUROCRYPT. New York: Springer, 2006.
[2] F. du Pin Calmon and N. Fawaz, "Privacy against
statistical inference," in Proc. Allerton Conf.
Communication, Control, and Computing, 2012,
pp. 1401-1408.
[3] S. Y. Kung, Kernel Methods and Machine Learning.
Cambridge, U.K.: Cambridge Univ. Press, 2014.
[4] N. Tishby, F. C. Pereira, and W. Bialek, "The information bottleneck method," in Proc. 37th Allerton
Conf. Communication and Computation, 1999.
[5] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern
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[6] S. Y. Kung, "Discriminant component analysis for
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[7] S. Y. Kung, T. Chanyaswad, J. Morris Chang, and
P. Wu, "Collaborative PCA/DCA learning methods for
compressive privacy," ACM Trans. Embed. Comput.
Syst. (Special Issue on Effective Divide-and-Conquer,
Incremental, or Distributed Mechanisms of
Embedded Designs for Extremely Big Data in LargeScale Devices), to be published.

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