Combined Channel Coding Research Articles

New detection algorithm with reduced complexity

A new algorithm for detecting and/or decoding of signals in communication system is presented. The advantage of the proposed suboptimum algorithm with respect to the classical maximum likelihood detection (MLSE) method lies in the significant reduction in computation complexity attainable by associating a reliable information to each received signal. At the same time, the error probability is maintained close to MLSE levels through the proper selection of the reliability information. We describe the application of the proposed algorithm to some interesting cases, such as the detection of continuous phase modulations with and without combined channel coding, detection of Ungerboeck codes, use in conjunction with the Viterbi algorithm. In all cases, a net reduction in computation complexity with respect to the classical MLSE algorithm is achieved.

Fundamental studies on combined coding for information source and channel

AbstractFrom both theoretical and practical viewpoints, one of the most basic and important problems in the digital communication system is to minimize the distortion between the output signal from the information source and the destination signal on the receiving side, under the condition that the transmission rate in the channel is fixed. From such a viewpoint, this paper presents a detailed investigation of the trade‐off relation between the vector quantization (source coding) and the error‐correcting coding (channel coding), where the transmission rate of the channel is fixed as constant. The combined source and channel coding is shown, which is optimal in the sense that the average overall distortion is minimized in the communication system. The minimized average distortion is compared with the average overall distortion, where the ideal error‐correcting code with the minimum distance given by Hamming's upper bound is applied. A detailed discussion is made. The memoryless Gaussian source, the squared‐error distortion measure, the binary symmetric channel and the binary (n, k) block error‐correcting code are assumed. Finally, an investigation is made using the actual speech communication system.

Compressors for combined source and channel coding with applications to the generalized Gaussian family (Corresp.)

The twin tools of asymptotic quantization theory and rate-distortion theory are used to find an expression for the approximate total mean-squared distortion of the series connection of a nonuniform quantizer and a noisy channel. The nonuniform quantizer is modeled as a compressor followed by a uniform quantizer. The approximate total mean-squared error distortion is minimized over a class of parameterized compressor characteristics for input processes whose univariate probability density functions are members of the generalized Gaussian family.