Boosting Belief Propagation for LDPC Codes with Deep Convolutional Neural Network Predictors

Abstract

Conventional channel codes are designed to recover channel errors by adding controlled redundancy to transmit bits; however, the main underlying assumption is that information bits are independent and identically distributed (i.i.d.). Short term and linear temporal correlations are assumed to be exploited by the preceding source encoders. This assumption is flawed in some scenarios since many types of data (e.g, audio samples, video frames, and sensor measurements) exhibit long-term relations and intricate dependencies that are not exploitable by conventional source encoders. Furthermore, sending plain information is still commonplace in wireless networks. Therefore, it is essential to design channel encoders that accommodate these conditions. It is well-known that the underlying hidden patterns can be captured by deep learning methods. This important capability is not yet fully utilized in channel encoder design. This work is a primary step towards developing a predictive channel decoder that learns the intricate dependencies within and between data frames using an embedded learning module at the receiver to enhance the bit decoding performance, especially in high-noise regimes. The learning module is integrated with the belief propagation algorithm over bipartite graphs appropriate for low-density parity-check (LDPC) codes. The proposed method is universal since no specific correlation model is adopted and the learning-based prediction is performed at the bit level. The proposed method is fully implemented at the receiver side, making it compatible with generic LDPC encoders. Our simulations demonstrate the superior performance of the proposed method compared to standard LDPC decoders. For instance, about 1.7 dB gain at the 10−4 BER level is achieved when recovering noisy audio files 11This material is based upon work supported by the National Science Foundation under Grants No. 1755984 and 2008784.