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| 003 | TR-AnTOB | ||
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| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 190711s2020 nju ob 001 0 eng d | ||
| 020 |
_a9781119562313 _q(epub) |
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| 020 |
_a1119562317 _q(epub) |
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| 020 |
_a9781119562276 _q(adobe pdf) |
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| 020 |
_a1119562279 _q(adobe pdf) |
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| 020 |
_a9781119562306 _q(electronic bk.) |
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| 020 |
_a1119562309 _q(electronic bk.) |
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| 020 |
_z9781119562252 _q(hardback) |
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| 020 |
_z1119562252 _q(hardback) |
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| 035 |
_a(OCoLC)1110125616 _z(OCoLC)1131862601 |
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| 040 |
_aDLC _beng _erda _cDLC _dOCLCO _dOCLCF _dDG1 _dCDN _dTR-AnTOB |
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| 041 | 0 | _aeng | |
| 042 | _apcc | ||
| 050 | 4 |
_aTK5103.2 _b.M3158 2020 |
|
| 050 | 0 | 0 | _aTK5103.2 |
| 090 |
_aTK5103.2 _b.M3158 2020EBK |
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| 245 | 0 | 0 |
_aMachine learning for future wireless communications / _cedited by Fa-Long Luo |
| 264 | 1 |
_aHoboken, NJ : _bWiley-IEEE, _c2020. |
|
| 300 | _a1 online resource (xxvi, 464 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 504 | _aBIBINDX | ||
| 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 |
||
| 650 | 0 |
_aWireless communication systems _0http://id.loc.gov/authorities/subjects/sh92006740 _9692 |
|
| 650 | 0 |
_aMachine learning _0http://id.loc.gov/authorities/subjects/sh85079324 _9738 |
|
| 650 | 0 |
_aNeural networks (Computer science) _0http://id.loc.gov/authorities/subjects/sh90001937 _9737 |
|
| 655 | 0 |
_aElectronic books _92032 |
|
| 700 | 1 |
_aLuo, Fa-Long, _0http://id.loc.gov/authorities/names/n96080443 _eeditor |
|
| 856 | 4 | 0 |
_3Wiley Online Library _zConnect to resource _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119562306 |
| 942 |
_2lcc _cEBK |
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