摘要
为了提升室内短距离可见光通信(VLC)的传输速率和通信质量,提出了一种基于正交时频空间(OTFS)调制和残差卷积神经网络(Res CNN)的VLC信道估计和均衡方法。首先,构建了OTFS调制下的室内VLC系统,并提出了基于训练序列的时频域信道估计模型。接着,利用Res CNN学习最小二乘(LS)信道估计到优化信道之间的映射关系,以解决符号间干扰问题并提高信道估计的准确性。最后,通过Res CNN对可见光信道进行估计和均衡。实验结果表明,在信号传输距离为1 m、传输速率为512 Mb/s~1.5 Gb/s时,该方法估计的误码率均低于3.8×10^(-3),有效提升了室内短距离VLC的传输速率和通信质量。
To improve the transmission rate and communication quality of indoor visible light communication(VLC),a VLC channel estimation and equalization method based on orthogonal time-frequency space(OTFS)modulation and residual convolutional neural network(ResCNN)is proposed.First,an indoor VLC system based on OTFS modulation is constructed,and a time-frequency domain channel estimation model based on training sequences is proposed.Then,ResCNN learns the mapping relationship between the least squares(LS)channel estimation and the optimized channel to solve the inter-symbol interference problem and improve the accuracy of channel estimation.Finally,the visible light channel is estimated and equalized using ResCNN.The experimental results show that when the signal transmission distance is 1 m and the transmission rate is 512 Mb/s to 1.5 Gb/s,the bit error rate estimated by the proposed method is lower than 3.8×10^(-3),effectively improving the transmission rate and communication quality of indoor short-distance VLC.
作者
姜彬
周鹏
JIANG Bin;ZHOU Peng(Intelligent Manufacturing and Information College,Jiangsu Shipping College,Nantong Jiangsu 226010,China)
出处
《光通信技术》
北大核心
2025年第1期38-43,共6页
Optical Communication Technology
基金
江苏省第五期333工程科研项目(BRA2018220)资助
南通市自然科学基金项目(JCZ2023029)资助。
关键词
可见光通信
短距离通信
信道估计
信道均衡化
深度学习
visible light communication
short distance communication
channel estimation
channel equalization
deep learning
