Power Allocation for NOMA-Based Visible Light Communication Systems with DQN

被引：0

作者：

Deng, Jiawei ^{[1
]}

Tang, Xuan ^{[1
]}

Wei, Xian ^{[2
]}

Li, Pu ^{[1
]}

Li, Jiaqi ^{[1
]}

Li, Xicong ^{[3
]}

Ghassemlooy, Zabih ^{[3
]}

机构：

[1] East China Normal Univ, Shanghai Key Lab Multidimens Informat Proc, Shanghai 200241, Peoples R China

[2] East China Normal Univ, Software Engn Inst, Shanghai, Peoples R China

[3] Northumbria Univ, Fac Engn & Environm, Opt Commun Res Grp, Newcastle Upon Tyne, Tyne & Wear, England

来源：

2024 14TH INTERNATIONAL SYMPOSIUM ON COMMUNICATION SYSTEMS, NETWORKS AND DIGITAL SIGNAL PROCESSING, CSNDSP 2024 | 2024年

关键词：

Deep reinforcement learning; NOMA; visible light communication; power allocation; NONORTHOGONAL MULTIPLE-ACCESS;

D O I：

10.1109/CSNDSP60683.2024.10636475

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The spectral efficiency of the visible light communication system can be enhanced by using the non-orthogonal multiple access (NOMA) scheme. In this paper, we propose a deep Q network (DQN) framework-based power allocation scheme that maximizes the sum rate of a NOMA-based cellular VLC network with mobility support. The numerical results indicate that the optimisation process achieves improved performance in terms of sum data rate (SDR) by approximately 8.8, 7, and 4% compared with conventional algorithms, such as fixed power allocation, gain ratio power allocation, and genetic algorithms.

引用

页码：512 / 517

页数：6

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