List decoding

About: List decoding is a research topic. Over the lifetime, 7251 publications have been published within this topic receiving 151182 citations.

List-decoding of subspace codes and rank-metric codes up to Singleton bound

Abstract: Subspace codes and rank-metric codes can be used to correct errors and erasures in network, with linear network coding. Both types of codes have been extensively studied in the past five years. Subspace codes were introduced by Koetter and Kschischang to correct errors and erasures in networks where topology is unknown (the non-coherent case). In this model, the codewords are vector subspaces of a fixed ambient space; thus codes for this model are collections of such subspaces. In a previous work, we have developed a family of subspace codes, based upon the Koetter-Kschichang construction, which are efficiently list decodable. Using these codes, we achieved a better decoding radius than Koetter-Kschischang codes at low rates. Herein, we introduce a new family of subspace codes based upon a different approach which leads to a linear-algebraic list-decoding algorithm. The resulting error-correction radius can be expressed as follows: for any integer s, our list-decoder using s + 1-variate interpolation polynomials guarantees successful recovery of the message sub-space provided the normalized dimension of errors is at most s(1 − sR). The same list-decoding algorithm can be used to correct erasures as well as errors. The size of output list is at most Qs − 1, where Q is the size of the field that message symbols are chosen from. Rank-metric codes are suitable for error correction in the case where the network topology and the underlying network code are known (the coherent case). Gabidulin codes are a well-known class of algebraic rank-metric codes that meet the Singleton bound on the minimum rank-distance of a code. In this paper, we introduce a folded version of Gabidulin codes analogous to the folded Reed-Solomon codes of Guruswami and Rudra along with a list-decoding algorithm for such codes. Our list-decoding algorithm makes it possible to recover the message provided that the normalized rank of error is at most 1 − R − ∊, for any ∊ > 0. Notably this achieves the information theoretic bound on the decoding radius of a rank-metric code.

Improved Log-MAP decoding algorithm for turbo-like codes

Abstract: In this letter, a new method for decoding turbo-like codes is proposed to simplify the hardware implementation of Log-MAP algorithm. In our method, the multivariable Jacobian logarithm in Log-MAP algorithm is actually concatenated by recursive 1D Jacobian logarithm units. Two new approximations of Log-MAP algorithm based on these 1D units are then presented, which have good approximated accuracy and is simple for hardware implementation. We further suggest a novel decoding scheme that its complexity is near the Max-Log-MAP while the performance is close to the Log-MAP algorithm.

Low Latency Coding: Convolutional Codes vs. LDPC Codes

Abstract: This paper compares the performance of convolutional codes to that of LDPC block codes with identical decoding latencies. The decoding algorithms considered are the Viterbi algorithm and stack sequential decoding for convolutional codes and iterative message passing for LDPC codes. It is shown that, at very low latencies, convolutional codes with Viterbi decoding offer the best performance, whereas for high latencies LDPC codes dominate - and sequential decoding of convolutional codes offers the best performance over a range of intermediate latency values. The "crossover latencies" - i.e., the latency values at which the best code/decoding selection changes - are identified for a variety of code rates (1/2, 2/3, 3/4, and 5/6) and target bit/frame error rates. For sequential decoding, both blockwise and continuous resynchronization procedures are used to allow the decoder to recover the correct path. The results indicate that sequential decoding substantially extends (beyond what is possible with Viterbi decoding) the range of latency values over which convolutional codes prove advantageous compared to LDPC block codes.

Two Informed Dynamic Scheduling Strategies for Iterative LDPC Decoders

Abstract: When residual belief-propagation (RBP), which is a kind of informed dynamic scheduling (IDS), is applied to low-density parity-check (LDPC) codes, the convergence speed in error-rate performance can be significantly improved. However, the RBP decoders presented in previous literature suffer from poor convergence error-rate performance due to the two phenomena explored in this paper. The first is the greedy-group phenomenon, which results in a small part of the decoding graph occupying most of the decoding resources. By limiting the number of updates for each edge message in the decoding graph, the proposed Quota-based RBP (Q-RBP) schedule can reduce the probability of greedy groups forming. The other phenomenon is the silent-variable-nodes issue, which is a condition where some variable nodes have no chance of contributing their intrinsic messages to the decoding process. As a result, we propose the Silent-Variable-Node-Free RBP (SVNF-RBP) schedule, which can force all variable nodes to contribute their intrinsic messages to the decoding process equally. Both the Q-RBP and the SVNF-RBP provide appealing convergence speed and convergence error-rate performance compared to previous IDS decoders for both dedicated and punctured LDPC codes.

Bit-error probability for maximum-likelihood decoding of linear block codes and related soft-decision decoding methods

Abstract: In this correspondence, the bit-error probability P/sub b/ for maximum-likelihood decoding of binary linear block codes is investigated. The contribution P/sub b/(j) of each information bit j to P/sub b/ is considered and an upper bound on P/sub b/(j) is derived. For randomly generated codes, it is shown that the conventional approximation at high SNR P/sub b//spl ap/(d/sub H//N).P/sub s/, where P/sub s/ represents the block error probability, holds for systematic encoding only. Also systematic encoding provides the minimum P/sub b/ when the inverse mapping corresponding to the generator matrix of the code is used to retrieve the information sequence. The bit-error performances corresponding to other generator matrix forms are also evaluated. Although derived for codes with a generator matrix randomly generated, these results are shown to provide good approximations for codes used in practice. Finally, for soft-decision decoding methods which require a generator matrix with a particular structure such as trellis decoding, multistage decoding, or algebraic-based soft-decision decoding, equivalent schemes that reduce the bit-error probability are discussed. Although the gains achieved at practical bit-error rates are only a fraction of a decibel, they remain meaningful as they are of the same orders as the error performance differences between optimum and suboptimum decodings. Most importantly, these gains are free as they are achieved with no or little additional circuitry which is transparent to the conventional implementation.