Adaptive Slepian-Wolf Decoding Based on Expectation Propagation

A major difficulty that plagues the practical use of Slepian-Wolf (SW) coding (and distributed source coding in general) is that the precise correlation among sources needs to be known a priori. However, belief propagation (BP) algorithm cannot adapt efficiently to the statistical change of the correlation. This paper proposes an adaptive SW decoding scheme which can perform online time-varying correlation estimation at the bit-level by incorporating expectation propagation (EP) algorithm. Moreover, we compare the proposed EP-based approach with Monte Carlo method using particle filtering (PF) algorithm. Our results show that the proposed EP estimator obtains the comparable estimation accuracy with less computational complexity than the PF method.