Open AccessResource allocation of fog radio access network based on deep reinforcement learning
Author(s) -
Tan Jingru,
Guan Wenbo
Publication year - 2022
Publication title -
engineering reports
Language(s) - English
Resource type - Journals
ISSN - 2577-8196
DOI - 10.1002/eng2.12497
Subject(s) - computer science , reinforcement learning , renewable energy , resource allocation , throughput , bandwidth (computing) , smart grid , grid , radio access network , access network , distributed computing , computer network , wireless , telecommunications , artificial intelligence , engineering , base station , electrical engineering , mobile station , geometry , mathematics
With the development of energy harvesting technologies and smart grid, the future trend of radio access networks will present a multi‐source power supply. In this article, joint renewable energy cooperation and resource allocation scheme of the fog radio access networks (F‐RANs) with hybrid power supplies (including both the conventional grid and renewable energy sources) is studied. In this article, our objective is to maximize the average throughput of F‐RAN architecture with hybrid energy sources while satisfying the constraints of signal to noise ratio (SNR), available bandwidth, and energy harvesting. To solve this problem, the dynamic power allocation scheme in the network is studied by using Q‐learning and Deep Q Network respectively. Simulation results show that the proposed two algorithms have low complexity and can improve the average throughput of the whole network compared with other traditional algorithms.