基于深度强化学习的可见光定位通信一体化功率分配研究

doi:10.11959/j.issn.1000-436x.2022163

Abstract

Abstract:

A power allocation scheme for integrated visible light position and communication (VLPC) system based on deep reinforcement learning was proposed to achieve power allocation for communication positioning integration.First, the frame structure design of integrated VLPC was proposed.Then the channel state information could be estimated by using the positioning information, and the CRLB of the positioning error was derived.Furthermore, the internal coupling relationship between positioning accuracy and communication rate was clarified.On this basis, a dynamic power allocation scheme based on deep deterministic policy gradient was proposed.Simulation results show that the proposed scheme can simultaneously achieve high-precision positioning and high-speed communication.

Key words: integrated VLPC, Cramér-Rao lower bound, power allocation, reinforcement learning

CLC Number:

TN92

Shuai MA, Bing LI, Haihong SHENG, Rongyan GU, Hui ZHOU, Hongmei WANG, Yue WANG, Shiyin LI. Research on power allocation of integrated VLPC based on deep reinforcement learning[J]. Journal on Communications, 2022, 43(8): 121-130.

Figures/Tables 9

参数	参数值
LED半功率角 $ψ_{\frac{1}{2}}$	60^D
接收机视场角?_FOV	90^D
折射率n_r	1.5
接收机感光面积A_r/cm²	1.0
聚光器增益Γ(?)/(A·W^-1)	1.0
滤光片增益F_s (A·W^-1)	1.0
噪声功率谱密度 $σ_{p}^{2}, σ_{c}^{2}$ /dBm	-98.82
信号幅度A/V	1.0
信号方差ε	1.0
直流偏置b/A	0.447 2
平均电功率P_e/W	1.0
最大光功率 $P_{o}^{\max}$ /W	2.236
总功率门限 $P_{total}^{}$ /W	3

References 24

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参数	参数值
回合数E	1 000
每回合总时隙T	20
经验池D大小	3 000
采样批次Z	32
优化器	Adam
折扣因子γ	0.9
Dropout	0.1
Actor网络学习率	0.000 2
Critic网络学习率	0.000 4
软更新系数τ	0.000 01

Research on power allocation of integrated VLPC based on deep reinforcement learning

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