基于人工神经网络的OFDM系统信道辨识与补偿  

Channel identification and compensation of OFDM system based on artificial neural network

作　　者：许癸驹 钱雅楠 代红英 Xu Guiju;Qian Ya′nan;Dai Hongying(College of Electronic Information,Chongqing Institute of Engineering,Chongqing 400056,China)

机构地区：[1]重庆工程学院电子信息学院,重庆400056

出　　处：《电子测量技术》2021年第8期120-124,共5页Electronic Measurement Technology

基　　金：重庆市教委科学技术研究计划项目(2020CQZX062)资助。

摘　　要：针对快衰落环境下OFDM系统的导频辅助信道估计,受到信道状态波动影响较大的问题,提出了一种基于人工神经网络的OFDM系统信道辨识与补偿方法。首先,对传统判决反馈信道估计方法进行了分析,说明了其在快衰落环境下存在的问题。其次,利用判决反馈信道估计以恒定间隔获得部分信道状态信息,从而仅用少量估计的信道状态信息来训练人工神经网络。然后,采用Levenberg-Marquardt算法进行神经网络训练。最后,在人工神经网络训练后,所有数据符号索引被串行输入到人工神经网络,以便对信道状态信息的整体转移进行插值,从而有效地补偿信道变化。快衰落环境下的OFDM通信系统测试结果表明,在多普勒频率为700 Hz的高移动性环境下,相比于传统估计方法,该方法表现出更好的误码率性能,可以消除错误平层,误码率达到10^(-4)以下。Aiming at the problem that the pilot aided channel estimation of OFDM system in fast fading environment is greatly affected by channel state fluctuation,a channel identification and compensation method for OFDM system based on artificial neural network is proposed.Firstly,the traditional decision feedback channel estimation method is analyzed,and its problems in fast fading environment are explained.Secondly,the decision feedback channel estimation is used to obtain part of the channel state information at constant intervals,so that only a small amount of estimated channel state information is used to train the artificial neural network.Then,Levenberg-Marquardt algorithm is used for neural network training.Finally,after the training of the artificial neural network,all data symbol indexes are serially input to the artificial neural network,so as to interpolate the whole transition of the channel state information,thus effectively compensating the channel variation.The test results of OFDM communication system in fast fading environment show that,compared with traditional estimation methods,this method has better BER performance in high mobility environment with Doppler frequency of 700 Hz,and can eliminate error flat layer,and the BER is below 10^(-4).

关 键 词：OFDM 快速衰落 非线性预测 人工神经网络 信道估计 

分 类 号：TN929.1[电子电信—通信与信息系统]