摘要
为了克服现有的非正交多址接入(NOMA)模型在可见光通信(VLC)系统中面临的室内多用户场景下多径效应与用户间干扰对通信可靠性的影响,提升频谱效率和通信速率,提出一种结合深度神经网络(DNN)信道估计与虚拟时间反转(VTR)信道均衡的NOMA-VLC系统。通过分析多用户NOMA-VLC信道特性,采用DNN进行精准信道估计,并利用VTR技术实现信道均衡,聚焦能量,抑制多径效应,增强通信可靠性和用户公平性。仿真结果表明:在两用户场景下,在误码率为10^(-3)时,系统性能分别提升了5.1 dB和4.9 dB,相较于其它算法,分别具有2 dB和2.4 dB的性能优势。
In order to overcome the impact of multipath effects and inter user interference on communication reliability in indoor multi-user scenarios faced by existing non orthogonal multiple access(NOMA)models in visible light communication(VLC)systems,improve spectral efficiency and communication rate,a NOMA-VLC system combining deep neural network(DNN)channel estimation and virtual time reversal(VTR)channel equalization is proposed.By analyzing the characteristics of multi-user NOMA-VLC channels,DNN is used for accurate channel estimation,and VTR technology is utilized to achieve channel equalization,focus energy,suppress multipath effects,enhance communication reliability and user fairness.The simulation results show that in a two user scenario,the system performance improved by 5.1 dB and 4.9 dB respectively at a bit error rate of 10^(-3),with performance advantages of 2 dB and 2.4 dB compared to other algorithms.
作者
徐美欣
张峰
赵黎
刘叶楠
XU Meixin;ZHANG Feng;ZHAO Li;LIU Yenan(School of Electronic Information Engineering,Xi'an Technological University,Xi'an 710021,China)
出处
《光通信技术》
北大核心
2025年第1期44-51,共8页
Optical Communication Technology
基金
陕西省科技计划项目(2024-YBXM-105)资助
西安市科技局高校院所科技人员服务企业项目(24GXFW0026)资助。
关键词
可见光通信
非正交多址
深度学习
时间反转技术
信道均衡
visible light communication
non-orthogonal multiple access
deep learning
time reversal technology
channel equalization
