6G短距离可见光通信信道估计与均衡OA北大核心
Channel estimation and equalization of short distance visible light communication in 6G
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(ResCNN)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ResCNN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ResCNN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3.8x10-3,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 convolu-tional neural network(ResCNN)is proposed.First,an indoor VLC system based on OTFS modulation is constructed,and a time-fre-quency 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 ex-perimental 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 3.8x103,effectively improving the transmission rate and communication quality of indoor short-distance VLC.
姜彬;周鹏
江苏航运职业技术学院智能制造与信息学院,江苏南通 226010江苏航运职业技术学院智能制造与信息学院,江苏南通 226010
电子信息工程
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 communicationshort distance communicationchannel estimationchannel equalizationdeep learning
《光通信技术》 2025 (1)
38-43,6
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.
评论