ISBN: 978-981-14-4787-7 DOI: 10.18178/wcse.2020.06.056
Power Allocation based on Q-Learning for NOMA Visible Light Communication Networks
Abstract— Non-orthogonal multiple access (NOMA) has been proposed to enhance system capacity for visible light communication (VLC) systems. However, the effective power allocation strategy is one of critical problems that needs to be solved in NOMA. In this paper, a new method for multi-user downlink power allocation in VLC NOMA based on reinforcement learning is proposed. This method utilizes distributed multi-agent Q-learning algorithm with low complexity to maximize sum throughput of the multiuser VLC downlink system which is subject to both user fairness and quality of service (QoS). The numerical results show that a large sum logarithmic user rate can be obtained with higher probability compared with other conventional power allocation algorithms
Index Terms— Visible light communication, non-orthogonal multiple access, power allocation, Q-learning
Ye Tian, Yufei Luo, Anhong Dang
Peking University, Department of Electronic, CHINA
Cite:Ye Tian, Yufei Luo, Anhong Dang" Power Allocation based on Q-Learning for NOMA Visible Light Communication Networks " Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp.372-380, Shanghai, China, 19-21 June, 2020.