Reliability-based maximum likelihood decoding (MLD) algorithms of linear block codes have been widely studied. These algorithms efficiently search the most likely codeword using the generator matrix whose most reliable and linearly independent k (dimension of the code) columns form the identity matrix. In this paper, conditions for omitting unnecessary metrics computation of candidate codewords are derived in reliability-based MLD algorithms. The proposed conditions utilize an order relation of binary vectors. A simple method for testing if the proposed conditions are satisfied is devised. The method for testing proposed conditions requires no real number operations and, consequently, the MLD algorithm employing this method reduces the number of real number operations, compared to known reliability-based MLD algorithms.

In this study, we develop a new algorithm for decoding binary linear codes for symbol-pair read channels. The symbol-pair read channel was recently introduced by Cassuto and Blaum to model channels with higher write resolutions than read resolutions. The proposed decoding algorithm is based on linear programming (LP). For LDPC codes, the proposed algorithm runs in time polynomial in the codeword length. It is proved that the proposed LP decoder has the maximum-likelihood (ML) certificate property, i.e., the output of the decoder is guaranteed to be the ML codeword when it is integral. We also introduce the fractional pair distance dfp of the code, which is a lower bound on the minimum pair distance. It is proved that the proposed LP decoder corrects up to ⌈dfp/2⌉-1 errors.

Maximum likelihood (ML) decoding of linear block codes can be considered as an integer linear programming (ILP). Since it is an NP-hard problem in general, there are many researches about the algorithms to approximately solve the problem. One of the most popular algorithms is linear programming (LP) decoding proposed by Feldman et al. LP decoding is based on the LP relaxation, which is a method to approximately solve the ILP corresponding to the ML decoding problem. Advanced algorithms for solving ILP (approximately or exactly) include cutting-plane method and branch-and-bound method. As applications of these methods, adaptive LP decoding and branch-and-bound decoding have been proposed by Taghavi et al. and Yang et al., respectively. Another method for solving ILP is the branch-and-cut method, which is a hybrid of cutting-plane and branch-and-bound methods. The branch-and-cut method is widely used to solve ILP, however, it is unobvious that the method works well for the ML decoding problem. In this paper, we show that the branch-and-cut method is certainly effective for the ML decoding problem. Furthermore the branch-and-cut method consists of some technical components and the performance of the algorithm depends on the selection of these components. It is important to consider how to select the technical components in the branch-and-cut method. We see the differences caused by the selection of those technical components and consider which scheme is most effective for the ML decoding problem through numerical simulations.

We consider the reliability-based heuristic search methods for maximum likelihood decoding, which generate test error patterns (or, equivalently, candidate codewords) according to their heuristic values. Some studies have proposed methods for reducing the space complexity of these algorithms, which is crucially large for long block codes at medium to low signal to noise ratios of the channel. In this paper, we propose a new method for reducing the time complexity of generating candidate codewords by storing some already generated candidate codewords. Simulation results show that the increase of memory size is small.

Recently, a word-valued source has been proposed as a new class of information source models. A word-valued source is regarded as a source with a probability distribution over a word set. Although a word-valued source is a nonstationary source in general, it has been proved that an entropy rate of the source exists and the Asymptotic Equipartition Property (AEP) holds when the word set of the source is prefix-free. However, when the word set is not prefix-free (non-prefix-free), only an upper bound on the entropy density rate for an i.i.d. word-valued source has been derived so far. In this paper, we newly derive a lower bound on the entropy density rate for an i.i.d. word-valued source with a finite non-prefix-free word set. Then some numerical examples are given in order to investigate the behavior of the bounds.

In channel decoding, a decoder with suboptimal metrics may be used because of the uncertainty of the channel statistics or the limitations of the decoder. In this case, the decoding metric is different from the actual channel metric, and thus it is called mismatched decoding. In this paper, applying the technique of the DS2 bound, we derive an upper bound on the error probability of mismatched decoding over a regular channel for the ensemble of linear block codes, which was defined by Hof, Sason and Shamai. Assuming the ensemble of random linear block codes defined by Gallager, we show that the obtained bound is not looser than the conventional bound. We also give a numerical example for the ensemble of LDPC codes also introduced by Gallager, which shows that our proposed bound is tighter than the conventional bound. Furthermore, we obtain a single letter error exponent for linear block codes.

Concatenated codes have many remarkable properties from both the theoretical and practical viewpoints. The minimum distance of a concatenated code is at least the product of the minimum distances of an outer code and an inner code. In this paper, we shall examine some cases that the minimum distance of concatenated codes is beyond the lower bound and get the tighter bound or the true minimum distance of concatenated codes by using the complete weight enumerator of the outer code and the Hamming weight enumerator of the inner code. Furthermore we propose a new decoding method based on Reddy-Robinson algorithm by using the decoding method beyond the BCH bound.

The joint source-channel coding problem is considered. The joint source-channel coding theorem reveals the existence of a code for the pair of the source and the channel under the condition that the error probability is smaller than or equal to ε asymptotically. The separation theorem guarantees that we can achieve the optimal coding performance by using the two-stage coding. In the case that ε = 0, Han showed the joint source-channel coding theorem and the separation theorem for general sources and channels. Furthermore the ε-coding theorem (0 ≤ ε <1) in the similar setting was studied. However, the separation theorem was not revealed since it is difficult in general. So we consider the separation theorem in this setting.

In this paper, we analyze the robustness for low-density parity-check (LDPC) codes over the Gilbert-Elliott (GE) channel. For this purpose we propose a density evolution method for the case where LDPC decoder uses the mismatched parameters for the GE channel. Using this method, we derive the region of tuples of true parameters and mismatched decoding parameters for the GE channel, where the decoding error probability approaches asymptotically to zero.

We study a modification method for constructing low-density parity-check (LDPC) codes for solid burst erasures. Our proposed modification method is based on a column permutation technique for a parity-check matrix of the original LDPC codes. It can change the burst erasure correction capabilities without degradation in the performance over random erasure channels. We show by simulation results that the performance of codes permuted by our method are better than that of the original codes, especially with two or more solid burst erasures.

This paper presents a new architecture for multiple-input signature analyzers. The proposed signature analyzer with Hδ inputs is designed by parallelizing a GLFSR(δ,m), where δ is the number of input signals and m is the number of stages in the feedback shift register. The GLFSR, developed by Pradhan and Gupta, is a general framework for representing LFSR-based signature analyzers. The parallelization technique described in this paper can be applied to any kind of GLFSR signature analyzer, e. g. , SISRs, MISRs, multiple MISRs and MLFSRs. It is shown that a proposed signature analyzer with Hδ inputs requires less complex hardware than either single GLFSR(Hδ,m)s or a parallel construction of the H original GLFSR(δ,m)s. It is also shown that the proposed signature analyzer, while requiring simpler hardware, has comparable aliasing probability with analyzers using conventional GLFSRs for some CUT error models of the same test response length and test time. The proposed technique would be practical for testing CUTs with a large number of output sequences, since the test circuit occupies a smaller area on the LSI chip than the conventional multiple-input signature analyzers of comparable aliasing probability.

In this paper, the existential lower bounds on asymptotic distance ratios of error-correcting codes with J(J2) levels of unequal error protection given by encoding and decoding methods based on the concept of the generalized concatenated codes are obtained. The constructive concatenated codes with unequal error protection are proposed for high and low code rates. The formaer is constructed by the generalized version of Justesen codes. The latter is constructed by the secondorder concatenation with the generalized concatenated codes. Comparing the existential lower bounds on asymptotic distance ratios with those of the constructive concatenated codes, the lower bounds of the constructive codes are lower than the existential lower bounds.

Orthogonal Arrays (OAs) have been playing important roles in the field of experimental design. It has been known that OAs are closely related to error-correcting codes. Therefore, many OAs can be constructed from error-correcting codes. But these OAs are suitable for only cases that equal interaction effects can be assumed, for example, all two-factor interaction effects. Since these cases are rare in experimental design, we cannot say that OAs from error-correcting codes are practical. In this paper, we define OAs with unequal strength. In terms of our terminology, OAs from error-correcting codes are OAs with equal strength. We show that OAs with unequal strength are closer to practical OAs than OAs with equal strength. And we clarify the relation between OAs with unequal strength and unequal error-correcting codes. Finally, we propose some construction methods of OAs with unequal strength from unequal error-correcting codes.

We propose a new fixed-rate error correction system with a feedback channel. In our system, the receiver transmits a list of positions of unreliable information bits based on the log a-posteriori probability ratios by outputs of a soft-output decoder to the transmitter. This method is just like that of the reliability-based hybrid ARQ scheme. To dynamically select an appropriate interleaving function with feedback information is a key feature of our system. By computer simulations, we show that the performance of a system with a feedback channel is improved by dynamically selecting an appropriate interleaving function.

F.P. Preparata et al. have proposed a fault diagnosis model to find all faulty units in the multicomputer system by using outcomes which each unit tests some other units. In this paper, for probabilistic diagnosis models, we show an efficient diagnosis algorithm to obtain a posteriori probability that each of units is faulty given the test outcomes. Furthermore, we propose a method to analyze the diagnostic error probability of this algorithm.

We propose transformation of a parity-check matrix of any low-density parity-check code. A code with transformed parity-check matrix is an equivalent of a code with the original parity-check matrix. For the binary erasure channel, performance of a message-passing algorithm with a transformed parity-check matrix is better than that with the original matrix.

We shall generalize B. S. Clarke and A. R. Barron 's analysis of the Bayes method for the FSMX sources. The FSMX source considered here is specified by the set of all states and its parameter value. At first, we show the asymptotic codelengths of individual sequences of the Bayes codes for the FSMX sources. Secondly, we show the asymptotic expected codelengths. The Bayesian posterior density and the maximum likelihood estimator satisfy asymptotic normality for the finite ergodic Markov source, and this is the key of our analysis.

A new ensemble of low-density parity-check (LDPC) codes for correcting a solid burst erasure is proposed. This ensemble is an instance of a combined matrix ensemble obtained by concatenating some LDPC matrices. We derive a new bound on the critical minimum span ratio of stopping sets for the proposed code ensemble by modifying the bound for ordinary code ensemble. By calculating this bound, we show that the critical minimum span ratio of stopping sets for the proposed code ensemble is better than that of the conventional one with keeping the same critical exponent of stopping ratio for both ensemble. Furthermore from experimental results, we show that the average minimum span of stopping sets for a solid burst erasure of the proposed codes is larger than that of the conventional ones.

Recently, Hof et al. extended the type-2 Duman and Salehi (DS2) bound to generalized decoding, which was introduced by Forney, with decision criterion FR. From this bound, they derived two significant bounds. One is the Shulman-Feder bound for generalized decoding (GD) with the binary-input output-symmetric channel. The other is an upper bound for an ensemble of linear block codes, by applying the average complete weight distribution directly to the DS2 bound for GD. For the Shulman-Feder bound for GD, the authors derived a condition under which an upper bound is minimized at an intermediate step and show that this condition yields a new bound which is tighter than Hof et al.'s bound. In this paper, we first extend this result for non-binary linear block codes used over a class of symmetric channels called the regular channel. Next, we derive a new tighter bound for an ensemble of linear block codes, which is based on the average weight distribution.

We analyze the extended stochastic complexity (ESC) which has been proposed by K. Yamanishi. The ESC can be applied to learning algorithms for on-line prediction and batch-learning settings. Yamanishi derived the upper bound of ESC satisfying uniformly for all data sequences and that of the asymptotic expectation of ESC. However, Yamanishi concentrates mainly on the worst case performance and the lower bound has not been derived. In this paper, we show some interesting properties of ESC which are similar to Bayesian statistics: the Bayes rule and the asymptotic normality. We then derive the asymptotic formula of ESC in the meaning of almost sure and mean convergence within an error of o(1) using these properties.