Open AccessDynamic Power Allocation in D2D Communications Using Deep Reinforcement Learning
Author(s) -
Jun Zhou
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2209/1/012017
Subject(s) - reinforcement learning , computer science , benchmark (surveying) , channel (broadcasting) , interference (communication) , scheme (mathematics) , distributed computing , artificial intelligence , computer network , mathematical analysis , mathematics , geodesy , geography
Due to the development of communication technology, mobile devices continue to increase. As one of the critical technologies of 5G, D2D communication is an up-and-coming technology. In this paper, multiple D2D pairs usually multiplex the same channel, which will cause serious channel interference. To solve this problem, a distributed deep reinforcement learning framework is proposed, which can well adapt to the power allocation in a dynamic environment. The simulation results show that compared with other benchmark methods, the proposed scheme can improve the overall D2D rate.