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000 camMu
001 2210080897335
003 OCoLC
005 20210225115012
006 m d
007 cr |n|||||||||
008 190703t20192019enk fo 000 0 eng d
019 a1137040424
020 a9781785616587
020 a1785616587
020 z1785616579
020 z9781785616570
035 a2339146b(NT)
035 a(OCoLC)1112080671z(OCoLC)1137040424
040 aSTFbengepncSTFdOCLCOdUIUdOCLCFdCUSdYDXdCNOdEBLCPdNdCSAd221008
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072 aB62502inspec
072 aC7410F2inspec
072 aC6170K2inspec
072 aC61302inspec
082 a621.3820285631
245 00 aApplications of machine learning in wireless communications /cedited by Ruisi He and Zhiguo Ding.
260 aLondon, United Kingdom :bThe Institution of Engineering and Technology,c2019.
300 a1 online resource (xvi, 474 pages).
336 atextbtxt2rdacontent
337 acomputerbc2rdamedia
338 aonline resourcebcr2rdacarrier
490 aIET Telecommunications series ;v81
504 aIncludes bibliographical references and index.
520 aIn such an era of big data where data mining and data analysis technologies are effective approaches for wireless system evaluation and design, the applications of machine learning in wireless communications have received a lot of attention recently. Machine learning provides feasible and new solutions for the complex wireless communication system design. It has been a powerful tool and popular research topic with many potential applications to enhance wireless communications, e.g. radio channel modelling, channel estimation and signal detection, network management and performance improvement, access control, resource allocation. However, most of the current researches are separated into different fields and have not been well organized and presented yet. It is therefore difficult for academic and industrial groups to see the potentialities of using machine learning in wireless communications. It is now appropriate to present a detailed guidance of how to combine the disciplines of wireless communications and machine learning.
588 aOnline resource; title from PDF title page (IET, viewed September 19, 2019).
590 aMaster record variable field(s) change: 650
650 aWireless communication systems.
650 aData mining.
650 aElectronic data processing.
650 aMachine learning.
650 aRadio.
650 aTelecommunicationxData processing.
650 aData mining.2fast0(OCoLC)fst00887946
650 aMachine learning.2fast0(OCoLC)fst01004795
650 aRadio.2fast0(OCoLC)fst01087053
650 aTelecommunicationxData processing.2fast0(OCoLC)fst01145844
650 aWireless communication systems.2fast0(OCoLC)fst01176209
650 aBig Data.2inspect
650 adata analysis.2inspect
650 adata mining.2inspect
650 alearning (artificial intelligence).2inspect
650 aradiocommunication.2inspect
650 atelecommunication computing.2inspect
653 amachine learning
653 awireless communications
653 aBig Data
653 adata mining
653 adata analysis
653 awireless system evaluation
653 awireless system design
655 aElectronic books.
655 aElectronic books.
700 aHe, Ruisi,eeditor.
700 aZhiguo Ding,eeditor.
776 iPrint version:tApplications of machine learning in wireless communications.dLondon, United Kingdom : The Institution of Engineering and Technology, 2019z1785616579w(OCoLC)1084807888
830 aIET telecommunications series ;v81.
856 3EBSCOhostuhttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2339146
938 aProQuest Ebook CentralbEBLBnEBL6026415
938 aYBP Library ServicesbYANKn301049516
938 aEBSCOhostbEBSCn2339146
994 a92bN
Applications of machine learning in wireless communications /edited by Ruisi He and Zhiguo Ding
Material type
전자책
Title
Applications of machine learning in wireless communications /edited by Ruisi He and Zhiguo Ding
Author's Name
He Ruisi editor Zhiguo Ding editor
Publication
London, United Kingdom : The Institution of Engineering and Technology 2019.
Physical Description
1 online resource (xvi, 474 pages)
Keyword
Includes bibliographical references and index. / In such an era of big data where data mining and data analysis technologies are effective approaches for wireless system evaluation and design, the applications of machine learning in wireless communications have received a lot of attention recently. Machine learning provides feasible and new solutions for the complex wireless communication system design. It has been a powerful tool and popular research topic with many potential applications to enhance wireless communications, e.g. radio channel modelling, channel estimation and signal detection, network management and performance improvement, access control, resource allocation. However, most of the current researches are separated into different fields and have not been well organized and presented yet. It is therefore difficult for academic and industrial groups to see the potentialities of using machine learning in wireless communications. It is now appropriate to present a detailed guidance of how to combine the disciplines of wireless communications and machine learning.
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MMissing Book Request
CClosed Stack Request
IInter-Campus Loan
CPriority Cataloging
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