光通信技术2024，Vol.48Issue(2)：18-23,6.DOI:10.13921/j.cnki.issn1002-5561.2024.02.004
基于深度学习的室内可见光通信光源配置策略
Light source configuration strategy for indoor visible light communication based on deep learning
摘要
Abstract
In order to seek the optimal light source optimization scheme in different indoor environments,a deep learning based indoor visible light communication light source configuration strategy is proposed.Introducing indoor obstacles,natural light,and human movement interference separately,with indoor signal-to-noise ratio uniformity as the objective function,using the flower pollination algorithm(FPA)to optimize the light source power and half power angle under different room conditions.Using the obtained room state and light source configuration as the training set,a convolutional neural network(CNN)is used for training.The resulting computational model can predict the optimal light source settings in different room states,achieving dynamic ad-justment of light source configuration.The simulation results show that the qualification rate of the light source parameters of this strategy reaches 88%,and the predicted results meet the requirements in terms of room signal power and lighting intensity.关键词
可见光通信/光源布局策略/花授粉算法/卷积神经网络/信噪比均匀度Key words
visible light communication/light source layout scheme/flower pollination algorithm/convolutional neural network/signal noise ratio uniformity分类
信息技术与安全科学引用本文复制引用
黄名川,王旭东,吴楠..基于深度学习的室内可见光通信光源配置策略[J].光通信技术,2024,48(2):18-23,6.基金项目
国家自然科学基金项目(61371091,61801074)资助. （61371091,61801074）
