Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142886
Title: Reinforcement learning-based intelligent resource allocation for integrated VLCP systems
Authors: Yang, Helin
Du, Pengfei
Zhong, Wen-De
Chen, Chen
Alphones, Arokiaswami
Zhang, Sheng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Yang, H., Du, P., Zhong, W.-D., Chen, C., Alphones, A., & Zhang, S. (2019). Reinforcement learning-based intelligent resource allocation for integrated VLCP systems. IEEE Wireless Communications Letters, 8(4), 1204-1207. doi:10.1109/lwc.2019.2911682
Project: SMA-RP6
Journal: IEEE Wireless Communications Letters
Abstract: In this letter, an intelligent resource allocation framework based on model-free reinforcement learning (RL) is first presented for multi-user integrated visible light communication and positioning (VLCP) systems, in order to maximize the sum rate of users while guaranteeing the users' minimum data rates and positioning accuracy constraints. The learning framework can learn the optimal policy under unknown environment's dynamics and the continuous-valued space, and a reward function is proposed to take into account the strict communication and positioning constraints. Moreover, a modified experience replay actor-critic (MERAC) RL approach is proposed to improve the learning efficiency and convergence speed, which efficiently collects the reliable experience and utilizes the most useful knowledge from the memory. Numerical results show that the MERAC approach can effectively learn to satisfy the strict constraints and achieve the fast convergence speed.
URI: https://hdl.handle.net/10356/142886
ISSN: 2162-2337
DOI: 10.1109/lwc.2019.2911682
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/lwc.2019.2911682
Fulltext Permission: open
Fulltext Availability: With Fulltext
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