联合DNN信道估计与虚拟时间反转信道均衡的NOMA-VLC系统OA北大核心
NOMA-VLC system combining DNN channel estimation and virtual time reversal channel equalization
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-us-er NOMA-VLC channels,DNN is used for accurate channel estimation,and VTR technology is utilized to achieve channel equal-ization,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.
徐美欣;张峰;赵黎;刘叶楠
西安工业大学电子信息工程学院,西安 710021西安工业大学电子信息工程学院,西安 710021西安工业大学电子信息工程学院,西安 710021西安工业大学电子信息工程学院,西安 710021
电子信息工程
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 communicationnon-orthogonal multiple accessdeep learningtime reversal technologychannel e-qualization
《光通信技术》 2025 (1)
44-51,8
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.
评论