DNN Aided Joint Source-Channel Decoding Scheme for Polar Codes

Abstract

In this letter, a deep neural network (DNN) aided joint source-channel (JSCC) decoding scheme is proposed for polar codes. In the proposed scheme, an integrated factor graph with an unfolded structure is first designed. Then a DNN aided flooding belief propagation decoding (FBP) algorithm is proposed based on the integrated factor, in which both source and channel scaling parameters in the BP decoding are optimized for better performance. Experimental results show that, with the proposed DNN aided FBP decoder, the polar coded JSCC scheme can have about 2-2.5 dB gain over different source statistics p with source message length N_SC = 128 and 0.2-1 dB gain over different source statistics p with source message length N_SC = 512 over the polar coded JSCC system with existing BP decoder.