000			03813cam a22006258i 4500
001			on1110125616
003			OCoLC
005			20220711203541.0
006			m o d
007			cr |||||||||||
008			190711s2020 nju ob 001 0 eng
010			_a 2019029934
040			_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dDG1 _dCDN
019			_a1131862601
020			_a9781119562313 _q(epub)
020			_a1119562317 _q(epub)
020			_a9781119562276 _q(adobe pdf)
020			_a1119562279 _q(adobe pdf)
020			_a9781119562306 _q(electronic bk.)
020			_a1119562309 _q(electronic bk.)
020			_z9781119562252 _q(hardback)
020			_z1119562252 _q(hardback)
029	1		_aAU@ _b000065844975
029	1		_aCHVBK _b582548861
029	1		_aCHNEW _b001076901
035			_a(OCoLC)1110125616 _z(OCoLC)1131862601
042			_apcc
050		4	_aTK5103.2 _b.M3158 2020
050	0	0	_aTK5103.2
082	0	0	_a621.3840285/631 _223
049			_aMAIN
245	0	0	_aMachine learning for future wireless communications / _cedited by Fa-Long Luo.
263			_a1911
264		1	_aHoboken, NJ : _bWiley-IEEE, _c2020.
300			_a1 online resource (xxvi, 464 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes 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"-- _cProvided by publisher.
588			_aDescription based on print version record and CIP data provided by publisher; resource not viewed.
590			_aJohn Wiley and Sons _bWiley Frontlist Obook All English 2020
650		0	_aWireless communication systems. _93474
650		0	_aMachine learning. _91831
650		0	_aNeural networks (Computer science) _93414
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795 _91831
650		7	_aNeural networks (Computer science) _2fast _0(OCoLC)fst01036260 _93414
650		7	_aWireless communication systems. _2fast _0(OCoLC)fst01176209 _93474
655		4	_aElectronic books. _93294
700	1		_aLuo, Fa-Long, _eeditor. _98710
776	0	8	_iPrint version: _aLuo, Fa-Long. _tMachine learning for future wireless communications _dHoboken, NJ : Wiley-IEEE, 2019. _z9781119562252 _w(DLC) 2019029933
856	4	0	_uhttps://doi.org/10.1002/9781119562306 _zWiley Online Library
942			_cEBK
994			_a92 _bDG1
999			_c69179 _d69179