Machine learning for future wireless communications / edited by Fa-Long Luo
Material type:
- text
- computer
- online resource
- 9781119562313
- 1119562317
- 9781119562276
- 1119562279
- 9781119562306
- 1119562309
- TK5103.2 .M3158 2020
- TK5103.2
Item type | Current library | Home library | Collection | Call number | Status | Notes | Date due | Barcode | |
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Merkez Kütüphane | Merkez Kütüphane | E-Kitap Koleksiyonu | TK5103.2 .M3158 2020EBK (Browse shelf(Opens below)) | Geçerli değil-e-Kitap / Not applicable-e-Book | EBK01541 |
Browsing Merkez Kütüphane shelves, Collection: E-Kitap Koleksiyonu Close shelf browser (Hides shelf browser)
TK5103.2EBK Smart Communications, Intelligent Algorithms and Interactive Methods Proceedings of 4th International Conference on Wireless Communications and Applications (ICWCA 2020) / | TK5103.2EBK Proceedings of International Conference on Wireless Communication ICWiCom 2021 / | TK5103.2 .E34 2021EBK Toward 6G : a new era of convergence / | TK5103.2 .M3158 2020EBK Machine learning for future wireless communications / | TK5103.2 .U48 2019EBK Ultra-dense networks for 5G and beyond : modelling, analysis, and applications / | TK5103.25 .A14 2020EBK 5G new radio : a beam-based air interface / | TK5103.25 .A148 2020EBK 5G radio access network architecture : the dark side of 5G / |
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"-- Provided by publisher
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