Momentum-Based Symbol Flipping Decoding Algorithms for Non-Binary LDPC Codes

Abstract

This paper investigates the application of gradient descent with momentum in symbol flipping decoding algorithms based on prediction (SFDP) for non-binary low-density parity-check (NB-LDPC) codes. The momentum added in the objective function of SFDP algorithms can provide inertia to the decoding process by considering the flipping states in the past iterations. Simulation results show that the proposed momentum-based SFDP algorithms perform significantly better than the original SFDP algorithms with low extra complexity. Furthermore, to lower the error floor of momentum-based SFDP algorithms, we also introduce artificial noises into the objective functions of momentum-based SFDP algorithms to help the iterative decoding escape from local optimum.