Lowering Error Floors Using Dithered Belief Propagation
François Leduc-Primeau, Saied Hemati, Shie Mannor, Warren J. Gross · 2010
We propose dithered belief propagation decoding algorithms to reduce the number of decoding failures of a belief propagation decoder and lower the error floor. The random nature of the algorithms enables a low hardware complexity compared to previously reported techniques. We introduce two dithering methods that target check node operations and channel input values, respectively. We present simulation results that confirm the error rate gains in the floor region, and that relate those gains with the maximum number of decoding iterations. The results show that the first algorithm can achieve good error rate gains with a low iteration limit. For the second algorithm, results show that with a large iteration limit, high FER gains are possible. Furthermore the average time complexity remains the same as that of a standard belief propagation algorithm.