Structured LDPC over URN model channels with memory

In this paper we investigate the performance of structured LDPC codes over a class of channels with memory. The channels under consideration are based on Polya's urn model [1] and can be viewed as a practical representative of channels with burst errors. Structured rather than random-like LDPC codes are considered due to the fact that the former lead to significant reduction in storage complexity and simple hardware architectures. The bit error rate (BER) curves for iterative decoding with channel estimation presented in this work show that random-like codes and a new class of regular and irregular codes based on parity-check matrices of the form of block-circulants have comparable performance for codelengths of the order of several thousands. Furthermore, the complete convergence region within the parameter space of the um channel model is determined by using a new technique for generating side-information termed state XORing. This technique also allows for complete characterization of the convergence region of another important bursty channel model, namely the Gilbert-Eliot scheme. Some possible applications of the results described in this paper are for designing reliable coding schemes for communication over fading or storage channels.