Recursive error correction for general Reed-Muller codes

Reed-Muller (RM) codes of growing length n and distance d are considered over a binary symmetric channel. A recursive decoding algorithm is designed that has complexity of order nlogn and corrects most error patterns of weight (dlnd)/2. The presented algorithm outperforms other algorithms with nonexponential decoding complexity, which are known for RM codes. We evaluate code performance using a new probabilistic technique that disintegrates decoding into a sequence of recursive steps. This allows us to define the most error-prone information symbols and find the highest transition error probability p, which yields a vanishing output error probability on long codes.     

Monotonic optimization based decoding for linear codes

New efficient methods are developed for the optimal maximum-likelihood (ML) decoding of an arbitrary binary linear code based on data received from any discrete Gaussian channel. The decoding algorithm is based on monotonic optimization that is minimizing a difference of monotonic (d.m.) objective functions subject to the 0–1 constraints of bit variables. The iterative process converges to the global optimal ML solution after finitely many steps. The proposed algorithm’s computational complexity depends on input sequence length k which is much less than the codeword length n, especially for a codes with small code rate. The viability of the developed is verified through simulations on different coding schemes.     

Generalisation of the Viterbi algorithm

The Viterbi algorithm, derived using dynamic programming techniques,is a maximum a posteriori (MAP) decoding method which was developedin the electrical engineering literature to be used in the analysisof hidden Markov models (HMMs). Given a particular HMM, theoriginal algorithm recovers the MAP state sequence underlyingany observation sequence generated from that model. This paperintroduces a generalization of the algorithm to recover, forarbitrary L, the top L most probable state sequences, with specialreference to its use in the area of automatic speech recognition.     

Peterson–Gorenstein–Zierler algorithm for skew RS codes

We design a non-commutative version of the Peterson–Gorenstein–Zierler decoding algorithm for a class of codes that we call skew RS codes. These codes are left ideals of a quotient of a skew polynomial ring, which endow them of a sort of non-commutative cyclic structure. Since we work over an arbitrary field, our techniques may be applied both to linear block codes and convolutional codes. In particular, our decoding algorithm applies for block codes beyond the classical cyclic case.     

Applications of convex optimization in signal processing and digital communication

 In the last two decades, the mathematical programming community has witnessed some spectacular advances in interior point methods and robust optimization. These advances have recently started to significantly impact various fields of applied sciences and engineering where computational efficiency is essential. This paper focuses on two such fields: digital signal processing and communication. In the past, the widely used optimization methods in both fields had been the gradient descent or least squares methods, both of which are known to suffer from the usual headaches of stepsize selection, algorithm initialization and local minima. With the recent advances in conic and robust optimization, the opportunity is ripe to use the newly developed interior point optimization techniques and highly efficient software tools to help advance the fields of signal processing and digital communication. This paper surveys recent successes of applying interior point and robust optimization to solve some core problems in these two fields. The successful applications considered in this paper include adaptive filtering, robust beamforming, design and analysis of multi-user communication system, channel equalization, decoding and detection. Throughout, our emphasis is on how to exploit the hidden convexity, convex reformulation of semi-infinite constraints, analysis of convergence, complexity and performance, as well as efficient practical implementation. Received: January 22, 2003 / Accepted: April 29, 2003 Published online: May 28, 2003 RID="*" ID="*" This research was supported in part by the Natural Sciences and Engineering Research Council of Canada, Grant No. OPG0090391, and by the Canada Research Chair program. New address after April 1, 2003: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA     

Fast Decoding Algorithms for First Order Reed-Muller and Related Codes

Fast decoding algorithms for short codes based on modifications of maximum likelihood decoding algorithms of first order Reed-Muller codes are described. Only additions-subtractions, comparisons and absolute value calculations are used in the algorithms. Soft and hard decisions maximum likelihood decoding algorithms for first order Reed-Muller and the Nordstrom-Robinson codes with low complexity are proposed.     

Fast skew-feedback shift-register synthesis

A skew-feedback shift-register is a generalization of a linear-feedback shift-register and can be applied in decoding (interleaved) Reed–Solomon codes or Gabidulin codes beyond half their code distance. A fast algorithm is proposed which synthesizes all shortest skew-feedback shift-registers generating L sequences of varying length over a field. For fixed L, the time complexity of the algorithm is ${{\mathcal O}(M(N) \log N)}$ operations, where N is the length of a longest sequence and M(N) is the complexity of the multiplication of two skew polynomials of maximum degree N.     

A redundancy detection algorithm for fuzzy stochastic multi-objective linear fractional programming problems

The computational complexity of linear and nonlinear programming problems depends on the number of objective functions and constraints involved and solving a large problem often becomes a difficult task. Redundancy detection and elimination provides a suitable tool for reducing this complexity and simplifying a linear or nonlinear programming problem while maintaining the essential properties of the original system. Although a large number of redundancy detection methods have been proposed to simplify linear and nonlinear stochastic programming problems, very little research has been developed for fuzzy stochastic (FS) fractional programming problems. We propose an algorithm that allows to simultaneously detect both redundant objective function(s) and redundant constraint(s) in FS multi-objective linear fractional programming problems. More precisely, our algorithm reduces the number of linear fuzzy fractional objective functions by transforming them in probabilistic–possibilistic constraints characterized by predetermined confidence levels. We present two numerical examples to demonstrate the applicability of the proposed algorithm and exhibit its efficacy.     

Consolidation for compact constraints and Kendall tau LP decodable permutation codes

Invented in the 1960s, permutation codes have reemerged in recent years as a topic of great interest because of properties making them attractive for certain modern technological applications, especially flash memory. In 2011 a polynomial time algorithm called linear programming (LP) decoding was introduced for a class of permutation codes where the feasible set of codewords was a subset of the vertex set of a code polytope. In this paper we investigate a new class of linear constraints for matrix polytopes with no fractional vertices through a new concept called “consolidation.” We then introduce a necessary and sufficient condition for which LP decoding methods, originally designed for the Euclidean metric, may be extended to provide an efficient decoding algorithm for permutation codes with the Kendall tau metric.     

Bounds on collaborative decoding of interleaved Hermitian codes and virtual extension

We derive the maximum decoding radius for interleaved Hermitian (IH) codes if a collaborative decoding scheme is used. A decoding algorithm that achieves this bound, which is based on a division decoding algorithm, is given. Based on the decoding radius for the interleaved codes, we derive a bound on the code rate below which virtual extension of non-interleaved Hermitian codes can improve the decoding capabilities.     

Equivalences between maximum a posteriori inference in Bayesian networks and maximum expected utility computation in influence diagrams

Two important tasks in probabilistic reasoning are the computation of the maximum posterior probability of a given subset of the variables in a Bayesian network (MAP), and the computation of the maximum expected utility of a strategy in an influence diagram (MEU). Both problems are NP^PP-hard to solve, and NP-hard to approximate when the treewidth of the underlying graph is bounded. Despite the similarities, researches on both problems have largely been conducted independently, with algorithmic solutions and insights designed for one problem not (trivially) transferable to the other one. In this work, we show constructively that these two problems are equivalent in the sense that any algorithm designed for one problem can be used to solve the other with small overhead. Moreover, the reductions preserve the boundedness of treewidth. Building on the known complexity of MAP on networks whose parameters are imprecisely specified, we show how to use the reductions to characterize the complexity of MEU when the parameters are set-valued. These equivalences extend the toolbox of either problem, and shall foster new insights into their solution.     

On maximum length convolutional codes under a trellis complexity constraint

We look at convolutional codes with maximum possible code length for prescribed redundancy, conditioned on constraints on the free distance and on the bit-oriented trellis state complexity. Rate (n−1)/n codes have been studied to some extent in the literature, but more general rates have not been studied much. In this work we consider convolutional codes of rate (n−r)/n, r⩾1. Explicit construction techniques for free distance d_free=3 and 4 codes are described. For codes with r=2, an efficient exhaustive search algorithm is outlined. For the more general case with r⩾2, a heuristic approach is suggested. Several new codes were found for r=1 and in particular for r=2 and 3. Compared to previously known codes of similar free distance and complexity constraints, the new codes have either strictly higher rate, or the same rate but smaller low distance multiplicities.     

On Ordinary Words of Standard Reed-Solomon Codes over Finite Fields

Reed-Solomon codes are widely used to establish a reliable channel to transmit information in digital communication which has a strong error correction capability and a variety of efficient decoding algorithm.Usually we use the maximum likelihood decoding(MLD) algorithm in the decoding process of Reed-Solomon codes.MLD algorithm relies on determining the error distance of received word.Dür,Guruswami,Wan,Li,Hong,Wu,Yue and Zhu et al.got some results on the error distance.For the Reed-Solomon code C,the received word u is called an ordinary word of C if the error distance d(u,C) =n-deg u(x) with u(x) being the Lagrange interpolation polynomial of u.We introduce a new method of studying the ordinary words.In fact,we make use of the result obtained by Y.C.Xu and S.F.Hong on the decomposition of certain polynomials over the finite field to determine all the ordinary words of the standard Reed-Solomon codes over the finite field of q elements.This completely answers an open problem raised by Li and Wan in[On the subset sum problem over finite fields,Finite Fields Appl.14 (2008) 911-929].     

List decoding of number field codes

This paper presents a list decoding algorithm for the number field codes of Guruswami (IEEE Trans Inf Theory 49:594–603, 2003). The algorithm is an implementation of the unified framework for list decoding of algebraic codes of Guruswami, Sahai and Sudan (Proceedings of the 41st Annual Symposium on Foundations of Computer Science, 2000), specialised for number field codes. The computational complexity of the algorithm is evaluated in terms of the size of the inputs and field invariants.     

Using a Hybrid Genetic-Algorithm/Branch and Bound Approach to Solve Feasibility and Optimization Integer Programming Problems

The satisfiability problem in forms such as maximum satisfiability (MAX-SAT) remains a hard problem. The most successful approaches for solving such problems use a form of systematic tree search. This paper describes the use of a hybrid algorithm, combining genetic algorithms and integer programming branch and bound approaches, to solve MAX-SAT problems. Such problems are formulated as integer programs and solved by a hybrid algorithm implemented within standard mathematical programming software. Computational testing of the algorithm, which mixes heuristic and exact approaches, is described.     

基于自协调函数的信道容量和最大熵的计算

信道容量和最大熵的计算是信息论中的经典问题.讨论了利用自协调函数理论计算信道容量,尤其是带约束的信道容量的方法.将最大熵的计算作为信道容量计算的特殊情况.作为应用,在证明了单位成本信道容量函数的单峰性的基础上,提出了其相应的多项式时间算法.     

Decoding interleaved Reed–Solomon codes beyond their joint error-correcting capability

A new probabilistic decoding algorithm for low-rate interleaved Reed–Solomon (IRS) codes is presented. This approach increases the error correcting capability of IRS codes compared to other known approaches (e.g. joint decoding) with high probability. It is a generalization of well-known decoding approaches and its complexity is quadratic with the length of the code. Asymptotic parameters of the new approach are calculated and simulation results are shown to illustrate its performance. Moreover, an upper bound on the failure probability is derived.     

Pseudocodeword-free criterion for codes with cycle-free Tanner graph

Iterative decoding and linear programming decoding are guaranteed to converge to the maximum-likelihood codeword when the underlying Tanner graph is cycle-free. Therefore, cycles are usually seen as the culprit of low-density parity-check codes. In this paper, we argue in the context of graph cover pseudocodeword that, for a code that permits a cycle-free Tanner graph, cycles have no effect on error performance as long as they are a part of redundant rows. Specifically, we characterize all parity-check matrices that are pseudocodeword-free for such class of codes.     

Stochastic scheduling to minimize expected maximum lateness

This paper is concerned with the problems in scheduling a set of jobs associated with random due dates on a single machine so as to minimize the expected maximum lateness in stochastic environment. This is a difficult problem and few efforts have been reported on its solution in the literature. In this paper, we first derive a deterministic equivalent to the expected maximum lateness and then propose a dynamic programming algorithm to obtain the optimal solutions. The procedures to compute optimal solutions are initially developed in the case of deterministic processing times, and then extended to stochastic processing times following arbitrary probability distributions. Moreover, several heuristic rules are suggested to compute near-optimal solutions, which are shown to be highly efficient and accurate by computer-based experiments.