Abstract
Belief propagation (BP) is a powerful algorithm to decode the low-density parity check (LDPC) codes over the additive white Gaussian noise (AWGN) channel. The traditional BP algorithm cannot adapt efficiently to the statistical change of the AWGN channel. Particle filter is a algorithm to estimate a variable of interest as it evolves over time. In this paper, we use particle filter to estimate the noise power and feed back to the BP algorithm in real time. We found that compared with the traditional BP algorithm with fixed estimated noise power, BP algorithm based on particle filter not only give a good real-time estimate for the channel noise, but also achieve a lower decoding error rate.
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