Analysis Of Iterative Decoding Research Articles

On the minimum stopping sets of product codes

It is shown that the certain combinatorial structures called stopping sets have the important role in analysis of iterative decoding. In this paper, the number of minimum stopping sets of a product code is determined by the number of the minimum stopping sets of the corresponding component codes. As an example, the number of minimum stopping sets of the r-dimensional SPC product code is computed.

Convergence Analysis of Iterative Decoding for Binary CEO Problem

Estimation of a binary source using multiple observers, a variant of the so called Chief Executive Officer (CEO) problem, is considered. A low-complexity Distributed Joint Source Channel Coding (D-JSCC) based on the Parallel Concatenated Convolutional Codes (PCCC) is implemented in a cluster of sensors in a distributed fashion. Convergence of the iterative decoder is analyzed by utilizing EXtrinsic Information Transfer (EXIT) chart technique to determine the convergence region in terms of the sensors? observation accuracy and channel SNR, where the iterative decoder outperforms the non-iterative one. This leads to design of a bi-modal decoder that adaptively switches between the iterative and non-iterative modes in order to avoid inefficient iterative information exchange without compromising the resulting Bit Error Rate (BER). This adaptive decoding algorithm saves the computational power and decoding time by a factor of about 10 by avoiding unnecessary iterations.

Pseudocodeword Weights for LDPC Codes under Differential PSK Transmission over the Noncoherent AWGN Channel

This Letter considers the finite-length analysis of iterative decoding of low-density parity-check codes under differential binary-phase-shift-keying (DBPSK) and differential quadrature-phase-shift-keying (DQPSK) over the noncoherent additive-white-Gaussian-noise channel. Specifically, we derive the weight of the pseudocodewords of a Tanner graph under these signaling schemes and use them to explain the difference in error performance between coded DBPSK and coded DQPSK.

To the Theory of Low-Density Convolutional Codes. II

A theoretical and experimental analysis of iterative decoding of low-density convolutional (LDC) codes is given. Two families are investigated: homogeneous LDC codes and a convolutional code version of turbo-codes.