GPU Acceleration for Particle Filter based LDPC Decoding

A parallel belief propagation algorithm based on Particle Filtering (PF) for channel estimation and Low-Density Parity-Check (LDPC) decoding is presented in this paper based on Compute Unified Device Architecture (CUDA). The authors have found that compared with the traditional Belief Propagation (BP) algorithm with fixed estimated noise power, BP algorithm based on PF [1] not only gives a good real-time estimate for the channel noise, but it also achieves a lower decoding error rate. However, the implementation of PF algorithm increased the decoding complexity. As a new hardware and software architecture for addressing and managing computations, CUDA offers parallel data computing using the highly multithreaded coprocessor driven by very high memory bandwidth GPU. The parallel noise adaptive decoding algorithm, based on CUDA, allows variable nodes, factor nodes or particles to be updated simultaneously, thus provideing an efficient and fast way for implementing the decoder.