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Machine learning for future wireless...

Luo, Fa-Long.

Machine learning for future wireless communications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning for future wireless communications // edited by Fa-Long Luo.
other author:	Luo, Fa-Long.
Published:	Hoboken, NJ :Wiley-IEEE, : 2020.,
Description:	xxvi, 464 p. ;ill. ; : 27 cm.;
Subject:	Neural networks (Computer science) -
ISBN:	9781119562252 (hbk.) :

Machine learning for future wireless communications /
Machine learning for future wireless communications /edited by Fa-Long Luo. - Hoboken, NJ :Wiley-IEEE,2020. - xxvi, 464 p. ;ill. ;27 cm.

Includes bibliographical references and index.

"Due to its powerful nonlinear mapping and distribution processing capability, deep neural networks based machine learning technology is being considered as a very promising tool to attack the big challenge in wireless communications and networks imposed by the explosively increasing demands in terms of capacity, coverage, latency, efficiency (power, frequency spectrum and other resources), flexibility, compatibility, quality of experience and silicon convergence. Mainly categorized into the supervised learning, the unsupervised learning and the reinforcement learning, various machine learning algorithms can be used to provide a better channel modelling and estimation in millimeter and terahertz bands, to select a more adaptive modulation (waveform, coding rate, bandwidth, and filtering structure) in massive multiple-input and multiple-output (MIMO) technology, to design a more efficient front-end and radio-frequency processing (pre-distortion for power amplifier compensation, beamforming configuration and crest-factor reduction), to deliver a better compromise in self-interference cancellation for full-duplex transmissions and device-to-device communications, and to offer a more practical solution for intelligent network optimization, mobile edge computing, networking slicing and radio resource management related to wireless big data, mission critical communications, massive machine-type communications and tactile internet"--

ISBN: 9781119562252 (hbk.) :NT4178Subjects--Topical Terms:

528588
Neural networks (Computer science)

LC Class. No.: TK5103.2 / .M3158 2020

Dewey Class. No.: 621.3840285/631

Machine learning for future wireless communications /
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020 $a 9781119562252 (hbk.) : $c NT4178
020 $a 1119562252 (hbk.)
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020 $z 1119562309 (ebk.)
020 $z 1119562317 (epub)
020 $z 9781119562276 (adobe pdf)
020 $z 9781119562306 (ebk.)
020 $z 9781119562313 (epub)
035 $a (OCoLC)1110125616 $z (OCoLC)1131862601
035 $a 1110125616
040 $a DLC $b eng $e aacr2 $c DLC $d OCLCO $d OCLCF $d DG1 $d CDN
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050 # 4 $a TK5103.2 $b .M3158 2020
050 0 0 $a TK5103.2
082 0 0 $a 621.3840285/631 $2 23
245 0 0 $a Machine learning for future wireless communications / $c edited by Fa-Long Luo.
260 # $a Hoboken, NJ : $b Wiley-IEEE, $c 2020.
300 $a xxvi, 464 p. ; $b ill. ; $c 27 cm.
504 $a Includes bibliographical references and index.
520 # $a "Due to its powerful nonlinear mapping and distribution processing capability, deep neural networks based machine learning technology is being considered as a very promising tool to attack the big challenge in wireless communications and networks imposed by the explosively increasing demands in terms of capacity, coverage, latency, efficiency (power, frequency spectrum and other resources), flexibility, compatibility, quality of experience and silicon convergence. Mainly categorized into the supervised learning, the unsupervised learning and the reinforcement learning, various machine learning algorithms can be used to provide a better channel modelling and estimation in millimeter and terahertz bands, to select a more adaptive modulation (waveform, coding rate, bandwidth, and filtering structure) in massive multiple-input and multiple-output (MIMO) technology, to design a more efficient front-end and radio-frequency processing (pre-distortion for power amplifier compensation, beamforming configuration and crest-factor reduction), to deliver a better compromise in self-interference cancellation for full-duplex transmissions and device-to-device communications, and to offer a more practical solution for intelligent network optimization, mobile edge computing, networking slicing and radio resource management related to wireless big data, mission critical communications, massive machine-type communications and tactile internet"-- $c Provided by publisher.
650 # 0 $a Neural networks (Computer science) $3 528588
650 # 0 $a Machine learning. $3 561253
650 # 0 $a Wireless communication systems. $3 562740
700 1 # $a Luo, Fa-Long. $3 896526