سال انتشار:
2020
عنوان انگلیسی مقاله:
Deep Reinforcement Learning-based resource allocation strategy for Energy Harvesting-Powered Cognitive Machine-to-Machine Networks
ترجمه فارسی عنوان مقاله:
استراتژی تخصیص منابع مبتنی بر یادگیری تقویتی عمیق برای شبکه های شناختی ماشین به ماشین با قدرت برداشت انرژی
منبع:
Sciencedirect - Elsevier - Computer Communications, 160 (2020) 706-717. doi:10.1016/j.comcom.2020.07.015
نویسنده:
Yi-Han Xu a,b,∗, Yong-Bo Tian a, Prosper Komla Searyoh a, Gang Yu c, Yueh-Tiam Yong
چکیده انگلیسی:
Machine-to-Machine (M2M) communication is a promising technology that may realize the Internet of
Things (IoTs) in future networks. However, due to the features of massive devices and concurrent access
requirement, it will cause performance degradation and enormous energy consumption. Energy Harvesting-
Powered Cognitive M2M Networks (EH-CMNs) as an attractive solution is capable of alleviating the escalating
spectrum deficient to guarantee the Quality of Service (QoS) meanwhile decreasing the energy consumption
to achieve Green Communication (GC) became an important research topic. In this paper, we investigate
the resource allocation problem for EH-CMNs underlaying cellular uplinks. We aim to maximize the energy
efficiency of EH-CMNs with consideration of the QoS of Human-to-Human (H2H) networks and the available
energy in EH-devices. In view of the characteristic of EH-CMNs, we formulate the problem to be a decentralized
Discrete-time and Finite-state Markov Decision Process (DFMDP), in which each device acts as agent and
effectively learns from the environment to make allocation decision without the complete and global network
information. Owing to the complexity of the problem, we propose a Deep Reinforcement Learning (DRL)-based
algorithm to solve the problem. Numerical results validate that the proposed scheme outperforms other schemes
in terms of average energy efficiency with an acceptable convergence speed.
Keywords: Energy Harvesting | M2M communication | Resource allocation | Deep Reinforcement Learning
قیمت: رایگان
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