In this paper we propose a hybrid maximum likelihood decoding (MLD) for linear block codes. For the reliable data transmission over noisy channels, convolutional and block codes are widely used in most digital communication systems. Much more efficient algorithms have been found for using channel measurement information in the decoding of convolutional codes than in the decoding of block codes....

In Multiple-Input Multiple-Output (MIMO) systems, Maximum-Likelihood (ML) decoding is equivalent to £nding the closest lattice point in an N dimensional complex space. In [1], we have proposed several quasimaximum likelihood relaxation models for decoding in MIMO systems based on semi-de£nite programming. In this paper, we propose randomization algorithms that £nd a near-optimum solution of the...

The present paper is intended to present a robust multiplicative video watermarking scheme. In this regard, the video signal is segmented into 3-D blocks like cubes, and then, the 3-D wavelet transform is applied to each block. The low frequency components of the wavelet coefficients are then used for data embedding to make the process robust against both malicious and unintentional attacks. Th...

Maximum likelihood decoding algorithms for Gaussian MIMO linear channels are considered. Linearity over the field of real numbers facilitates the design of maximum likelihood decoders using number theoretic tools for searching the closest lattice point. These decoders are collectively referred to as sphere decoders in the literature. In this paper, a fresh look at this class of decoding algorit...

The reliability-based heuristic search methods for maximum likelihood decoding (MLD) generate test error patterns (or, equivalently, candidate codewords) according to their heuristic values. Test error patterns are stored in lists and its space complexity is crucially large for MLD of long block codes. One of the well-known heuristic search methods for MLD is the A decoding algorithm proposed b...

The reliability-based heuristic search methods for maximum likelihood decoding (MLD) generate test error patterns (or, equivalently, candidate codewords) according to their heuristic values. Test error patterns are stored in lists and its space complexity is crucially large for MLD of long block codes. Based on the decoding algorithms both of Battail and Fang and of its generalized version sugg...

We propose a novel decision-feedback maximum-likelihood decoder for fading channels. These channels have memory due to the fading correlation, and the complexity of maximum-likelihood decoding for such channels grows exponentially with memory length. Therefore, in practice, the encoded bit stream is typically interleaved prior to transmission and the deinterleaved bit stream is decoded as for a...

We suggest a decoding algorithm of -ary linear codes, which we call supercode decoding. It ensures the error probability that approaches the error probability of minimumdistance decoding as the length of the code grows. For the algorithm has the maximum-likelihood performance. The asymptotic complexity of supercode decoding is exponentially smaller than the complexity of all other methods known...

This paper presents a new decoder for quadra-ture-amplitude-modulated (QAM) signals transmitted through Markov modulated fading channels. Markov modulated channels arise in city environments when a direct line of sight is blocked intermittently, and for large area coverage mobile cells where the channel has large variations in fading statistics. The channel is modelled as a time varying AR proc...