Machine learning for future wireless communications / (Record no. 69179)

000 -LEADER
fixed length control field 03813cam a22006258i 4500
001 - CONTROL NUMBER
control field on1110125616
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220711203541.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190711s2020 nju ob 001 0 eng
019 ## -
-- 1131862601
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119562313
-- (epub)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119562317
-- (epub)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119562276
-- (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119562279
-- (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119562306
-- (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 1119562309
-- (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (hardback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- (hardback)
029 1# - (OCLC)
OCLC library identifier AU@
System control number 000065844975
029 1# - (OCLC)
OCLC library identifier CHVBK
System control number 582548861
029 1# - (OCLC)
OCLC library identifier CHNEW
System control number 001076901
082 00 - CLASSIFICATION NUMBER
Call Number 621.3840285/631
245 00 - TITLE STATEMENT
Title Machine learning for future wireless communications /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 online resource (xxvi, 464 pages)
520 ## - SUMMARY, ETC.
Summary, etc "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"--
590 ## - LOCAL NOTE (RLIN)
Local note John Wiley and Sons
700 1# - AUTHOR 2
Author 2 Luo, Fa-Long,
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1002/9781119562306
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Hoboken, NJ :
-- Wiley-IEEE,
-- 2020.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
520 ## - SUMMARY, ETC.
-- Provided by publisher.
588 ## -
-- Description based on print version record and CIP data provided by publisher; resource not viewed.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Wireless communication systems.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine learning.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Neural networks (Computer science)
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine learning.
-- (OCoLC)fst01004795
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Neural networks (Computer science)
-- (OCoLC)fst01036260
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Wireless communication systems.
-- (OCoLC)fst01176209
994 ## -
-- 92
-- DG1

No items available.