Reinforcement-Learning-Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks

This article proposes a novel approach to improve the energy efficiency (EE) of an energy-harvesting (EH)-enabled IoT network supported by simultaneous wireless information and power transfer (SWIPT). More specifically, the device-to-device (D2D) users harvest ambient energy throughout their communi...

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Veröffentlicht in:	IEEE internet of things journal Jg. 9; H. 17; S. 16521 - 16531
Hauptverfasser:	Omidkar, Atefeh, Khalili, Ata, Nguyen, Ha H., Shafiei, Hossein
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computational geometry Convexity Device-to-device (D2D) communications Device-to-device communication Energy Energy harvesting Internet of Things Internet of Things (IoT) networks Learning majorization–minimization (MM) Mixed integer mixed-integer nonlinear problem (MINLP) Optimization Optimization techniques Power control Power management Power transfer Quality of service reinforcement learning (RL) Resource allocation Resource management spectrum sharing Wireless communication
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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