Joint Channel Equalization and Decoding with One Recurrent Neural Network Channel equalization has been widely used to eliminate intersymbol interference (ISI) and improve transmission performance in fading channel. In this paper, we propose a novel model of joint channel equalization and decoding based on recurrent neural network (RNN) in order to recover information messages interfered by channel distortion. By returning the output of decoder to the input of equalizer, an iterative equalizing and decoding process is achieved. Simulation over linear channel shows our method offers performance near that of maximum likelihood (ML) equalizer with knowledge of perfect channel state information (CSI). With less than 23 of the parameters, the proposed model has more than 0.5 dB gain over the CNN  NNDJoint model (and three other models) over nonlinear channel.

Neural Networkbased Equalizer by Utilizing Coding Gain in Advance Recently, deep learning has been exploited in many fields with revolutionary breakthroughs. In the light of this, deep learningassisted communication systems have also attracted much attention in recent years and have potential to break down the conventional design rule for communication systems. In this work, we propose two kinds of neural networkbased equalizers to exploit different characteristics between convolutional neural networks and recurrent neural networks. The equalizer in conventional blockbased design may destroy the code structure and degrade the capacity of coding gain for decoder. On the contrary, our proposed approach not only eliminates channel fading, but also exploits the code structure with utilization of coding gain in advance, which can effectively increase the overall utilization of coding gain with more than 1.5 dB gain.