基于深度学习的6G可见光通信多址接入解调方法  

Deep learning based demodulation method for multiple access of visible light communication in 6G

作　　者：邵鑫玉 姚瑶[1] 王亮[1] SHAO Xinyu;YAO Yao;WANG Liang(Jiangsu Vocational College of Business,Nantong 226011,China)

机构地区：[1]江苏商贸职业学院,江苏南通226011

出　　处：《电信科学》2024年第8期121-129,共9页Telecommunications Science

摘　　要：发光二极管的调制带宽较窄,导致可见光通信系统的容量受限,通过多址接入技术可提高频谱效率和终端用户数量。然而,可见光通信系统多址接入的用户间存在较强的干扰。针对此问题,利用可见光通信系统接收信号间的相关性,提出一种基于深度神经网络的多址接入多用户检测与信号还原方法。基于稀疏码多址接入给出了可见光通信系统的发送端模型与接收端模型,采用时域卷积网络学习长序列的信号间时域相关性,再传入密集层学习信号序列的空间域映射关系,最终在可见光通信系统的接收端还原所有用户的信号。实验结果表明,该方法有效提高了可见光通信系统多址接入的通信性能,在不同通信距离、信噪比、发送速率下均能发挥积极作用。Because the modulation bandwidth of the light emitting diode is narrow,the capacity of the visible light communication system is limited,the spectral efficiency and number of endpoint user can be improved by the multiple access technique.However,there is strong inter-user interference among multiple access users in the visible light communication system.In view of this problem,by utilizing the correlation among received signals of the visible communication system,a multiple user detection and signal recovery method of multiple users for multiple access based on deep neural network was proposed.The transmitter model and the receiver model of the visible light communication system were presented based on sparse code multiple access,the temporal convolutional network was adopted to learn the inter-signal temporal correlation of the long sequence,the output sequence was delivered to dense layer to learn the spatial mapping relationship of the signal sequence,in the end,signals of all users were recovered in the receiver of the visible light communication system.Experimental results indicate that the proposed signal recov‐ery method improves the communication performance of the visible light communication system multiple access ef‐fectively,and the proposed method can play an active role under the condition of different communication distances,different signal noise ratios and transmit speeds.

关 键 词：可见光通信系统 多址接入 解调技术 短距离无线通信 卷积神经网络 

分 类 号：TN929.1[电子电信—通信与信息系统]