基于NOMA-VLC系统的GWO-DNN信道估计方法

GWO-DNN channel estimation method based on NOMA-VLC system

为了解决室内非正交多址(NOMA)可见光通信(VLC)信道估计方法的可靠性和用户公平性低问题,提出了一种在NOMA-VLC系统中使用灰狼优化(GWO)算法对深度神经网络(DNN)进行信号补偿和恢复的方法,即GWO-DNN信道估计方法。该方法引入非线性收敛因子和二维混沌映射,有效提升了系统可靠性和多用户传输公平性。实验结果表明:在误码率(BER)为10^(-4)时,所提方法比最小均方误差(MMSE)方法最高可获得4.3 dB的信噪比(SNR)增益,两用户的SNR差异由3.2 dB降到0.9 dB;在不同阶次的正交幅度调制下,所提方法的性能均优于MMSE方法,且调制阶次越高性能改善越明显。

In order to solve the problems of 1ow reliabilty and user fairness in indoor non-orthogonal multiple access(NOMA)visible light communication(VLC)channel estimation methods,a method of using the Gray Wolf optimizer(GWO)algorithm to compensate and restore signals in deep neural network(DNN)in NOMA-VLC systems,that is GWO-DNN channel estimation method.This method introduces nonlinear convergence factors and two-dimensional chaotic mapping.effectively improving sys-tem reliability and multi-user transmission faimess.Experimental results show that,at a bit error rate(BER)of 10^(-4),the proposed method can achieve a maximum signal-to noise ratio(SNR)gain of 4.3 dB compared to the minimum mean square error(MMSE)method,and the SNR difference between the two users is reduced from 3.2 dB to 0.9 dB.Under different orders of quadrature amplitude modulation,the performance of the proposed method is better than the MMSE method,and the higher the modulation order,the more significant the performance improvement.