Stochastic Decoding for LDPC Convolutional Codes

Abstract

Among LDPC codes, LDPC convolutional codes (LDPC-CCs) seem to be more suitable for variable length applications. However, a LDPC-CC decoder is difficult to implement for its long latency and large storage usage. The stochastic computation makes the decoding of LDPC-CCs more efficient, but the boundary effect of sliding window causes poor performance. In this paper, a stochastic LDPC-CC decoder with virtual edge compensation as well as decoder architecture is presented. The simulation results based on (491, 3, 6) time-varying LDPC-CC show that under the same signal-to-noise ratio, our proposed decoder could achieve better performance, 60% less decoding latency and 40% storage reduction compared to log-BP decoder with 10 processors.