Robust Blind Equalization Algorithm Using Convolutional Neural Network
Ruru Mei, Zhugang Wang, Wanru Hu
Abstract
In this paper, we introduce a novel blind equalization algorithm based on the convolutional neural network (CNN), to improve the bit error rate (BER) performance of equalizers against the multipath fading effect and nonlinear distortion. In contrast to existing neural network (NN)-based blind equalization algorithms, the proposed algorithm performs equalization and soft demapping jointly, which allows it to obtain soft bits directly from received data. In addition, the input preprocessing module is used to reorganize the received data to fully exploit the feature information of the received signals. Afterwards, the two-dimensional (2D) convolutions can be operated on a three-dimensional (3D) input array obtained by input preprocessing to recover the transmitted bits. Simulation results show that the proposed algorithm outperforms other CNN-based blind equalization algorithms, in terms of BER performance, under a wide range of channel models, signal-to-noise (SNR) levels and modulation schemes. Strikingly, the BER obtained by the proposed algorithm approaches the theoretical BER of multiple quadrature amplitude modulation (M-QAM) in linear channels.