In this paper, we introduce the optimization of SISO(Soft-Input SoftOutput) decoding for Hamming code which is well known as linear block code. When the problem that the output value becomes infitity during SISO decoding of Hamming code occurs, it is resolved by entering an appropriate value between 1 and 2 which is obtained through the experiment in the decoder. The simulation result has shown...

An efficient maximum-likelihood soft-decision decoding algorithm for linear block codes using a generalized Dijkstra’s algorithm was proposed by Han, Hartmann, and Chen. In this correspondence we prove that this algorithm is efficient for most practical communication systems where the probability of error is less than 10 3 by finding an upper bound of the computational effort of the algorithm. ...

Permutation codes are special spherical codes designed for the band-limited Gaussian channel. Here, we investigate their application to fading channels: we present a simple maximum likelihood decoding algorithm and compute expressions for the codeword error probability.

This article investigates efficient blind watermark decoding approaches for hidden messages embedded into host images, within the framework of additive spread spectrum (SS) embedding based for data hiding. We study SS embedding in both the discrete cosine transform and the discrete Fourier transform (DFT) domains. The contributions of this article are multiple-fold: first, we show that the conv...

This paper studies multiuser coding techniques for channel coding with a given (possibly suboptimal) decoding rule. For the mismatched discrete memoryless multiple-access channel, error exponents are obtained which are tight with respect to the ensemble average, and positive within the interior of Lapidoth’s achievable rate region. In the special case of maximum-likelihood decoding, the ensembl...

This study presents a novel technique to estimate the computational complexity of sequential decoding using the Berry-Esseen theorem. Unlike the theoretical bounds determined by the conventional central limit theorem argument, which often holds only for sufficiently large codeword length, the new bound obtained from the Berry-Esseen theorem is valid for any blocklength. The accuracy of the new ...

Maximum Likelihood Decoding (MLD) is computationally complex technique for decoding received information in multiple input multiple output (MIMO) systems. Tree search algorithms such as sphere decoding (SD) and QR decomposition with M survivals (QRD-M) are used to reduce the complexity keeping the performance near ML. This paper presents two techniques for reducing the computational complexitie...

In this paper we present a new and improved correlation attack based on maximum likelihood (ML) decoding. Previously the code rate used for decoding has typically been around r = 1/2. Our algorithm has low computational complexity and is able to use code rates around r = 1/2. This way we get much more information about the key bits. Furthermore, the run time for a successful attack is reduced s...

A Viterbi algorithm has served as a powerful method for decoding of the convolutional code so as to control errors in data transmission over a noisy channel. It is based on maximum likelihood algorithm for decoding the data. However, the hardware implementation of Viterbi algorithm become crucial as it consumes large resources due to its complexity. This paper discusses the implementation of an...

By tracing the flow of computations in the iterative decoders for low density parity check codes, we are able to formulate a signal-space view for a finite number of iterations in a finite-length code. On a Gaussian channel, maximum a posteriori codeword decoding (or “maximum likelihood decoding”) decodes to the codeword signal that is closest to the channel output in Euclidean distance. In con...