可见光通信系统的非线性失真抑制研究

Study on non-linear distortion suppression of visible light communication system

发光二极管的非线性特性是引起光信号出现非线性失真的一个重要因素,针对该问题,采用人工神经网络在接收端对信号的非线性失真进行抑制,进而降低可见光通信系统的误码率。将发光二极管的输入电信号与接收端转换后的电信号组成成对数据,将成对数据集送入神经网络进行训练,学习信号在电光转换、信道传输及光电转换过程中的非线性失真特性,通过神经网络对信号的非线性失真进行估计与抑制。此外,在训练过程中采用分布估计算法搜索神经网络的超参数集,以降低训练难度。实验结果表明,该方法在不同的信道环境下均能有效地改善可见光通信的性能。

The non-linear characteristic of the light emitting diode can give rise to non-linear distortion of the light signal,aiming at this problem,the artificial neural network is introduced to the receiver end to realize the non-linear distortion suppression for the signals,and then the bit error rate of the visible light communication is reduced.The input electrical signal of the light emitting diode and the transformed electrical signal of the receiver are combined paired-wise data,then the paired-wise data is collected to train the artificial neural network,and learn non-linear distortion characteristic of electric-optical conversion,channel transmission and optical-electric conversion.As a result,the non-linear distortion of signals is estimated and suppressed with the neural network.Besides,EDA is adopted to search the super parameters of the neural network during the training phase,to reduce the training difficulty.Experimental results show that,the proposed method can effectively improve the performance of the visible light communication with different channel conditions.