Joint Codebook Selection and MCS Adaptation for MmWave eMBB Services Based on Deep Reinforcement Learning

This article investigates the joint codebook selection and modulation-coding-scheme (MCS) adaptation issue for the enhanced mobile broadband (eMBB) service in millimeter-wave (mmWave) cellular systems. The proposed scheme guarantees efficient mmWave eMBB service through an intelligent joint codebook...

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Vydané v:	IEEE internet of things journal Ročník 11; číslo 19; s. 31545 - 31560
Hlavní autori:	Ye, Xiaowen, Fu, Liqun, Cioffi, John M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.10.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation Algorithms Broadband Closed loops Codebook selection Constraints Decision making Deep learning deep reinforcement learning (DRL) enhanced mobile broadband (eMBB) service Fading channels Internet of Things Millimeter wave communication Millimeter waves millimeter-wave (mmWave) communication Mobile computing modulation-coding-scheme (MCS) adaptation Optimization Time Training Ultra reliable low latency communication
ISSN:	2327-4662, 2327-4662
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