[Show abstract][Hide abstract] ABSTRACT: We present a novel stochastic decoding algorithm for Reed-Solomon codes. We apply an iterative Monte Carlo based approach called the Cross-Entropy method to produce, in every iteration, a set of random error locator vectors, each indicates n - k possible erasure positions within a received word. We associate each error locator vector with a candidate codeword by erasures-only decoding the received word, using the error locator vector to locate the erasures. Each iteration results in a new elite set that contains the best E candidate codewords. To increase the search radius and enhance the decoder performance we use the randomly drawn samples to generate what we call virtual received words from which extra candidate codewords and thus candidate elite members can be obtained. The proposed algorithms offer both complexity and performance advantages over some existing algebraic decoding algorithms for high rate RS codes.
[Show abstract][Hide abstract] ABSTRACT: Implementing a belief propagation (BP) based LDPC decoder requires high degrees of parallelism using many component soft-in soft-output (SISO) decoding units to perform message passing from variable nodes to check nodes or vice versa. An obvious complexity-reduction solution is to serialize the decoding process, i.e., dividing a decoding iteration into several serial sub-iterations in which a sub-iteration performs only part of the complete parallel message-passing operation. The group horizontal shuffled BP (GHSBP) and vertical shuffled BP (GVSBP) algorithms respectively partition the check and variable nodes of the code graph into groups to perform group-by-group message-passing decoding. This paper proposes new techniques to improve three key elements of a GHSBP decoding algorithm, namely, the grouping method, the decoding schedule and the log-likelihood updating formulae. The (check nodes) grouping method and decoding schedule optimize certain design criterion. The new normalized min-sum updating formula with a self-adjustable correction (scaling) factor offers better nonlinear approximation. Numerical performance of new GHSBP algorithms that include part or all three new techniques indicate that the combination of the proposed grouping and decoding schedule yields a faster convergence rate and our modified min-sum algorithm gives performance superior to that of the conventional min-sum and normalized min-sum algorithm and is very close to that of the sum-product algorithm.
[Show abstract][Hide abstract] ABSTRACT: Most investigations on the effect of channel memory on the performance of block codes use a two-state Gilbert-Elliott (GE) model to describe the channel behavior. As there are circumstances that the channel of concern can not be properly described by the GE model, there are some recent works on coded performance that characterize the channel behavior by a general finite-state Markov chain. This letter presents a new efficient systematic approach to analyze the performance of block codes in such a hidden Markov channel (HMC). An application example is given to predict codeword error probability performance of an RS-coded system in a channel with memory. Numerical results are also provided to validate our analytic results.
No preview · Article · Feb 2008 · IEEE Transactions on Communications