High-throughput VLSI Implementations of Iterative Decoders and Related Code Construction Problems
Journal of VLSI Signal Processing Systems
Decoding LDPC convolutional codes on Markov channels
EURASIP Journal on Wireless Communications and Networking
Density Evolution Analysis of Robustness for LDPC Codes over the Gilbert-Elliott Channel
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Capacity-achieving codes for finite-state channels with maximum-likelihood decoding
IEEE Journal on Selected Areas in Communications - Special issue on capaciyy approaching codes
On decoding binary perfect and quasi-perfect codes over Markov noise channels
IEEE Transactions on Communications
A channel representation method for the study of hybrid retransmission-based error control
IEEE Transactions on Communications
Ordering finite-state Markov channels by mutual information
IEEE Transactions on Information Theory
Capacity-achieving codes for channels with memory with maximum-likelihood decoding
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
A discrete channel model for capturing memory and soft-decision information: a capacity study
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Convolutionally coded transmission over Markov-Gaussian channels: analysis and decoding metrics
IEEE Transactions on Communications
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Density evolution analysis of low-density parity-check (LDPC) codes in memoryless channels is extended to the Gilbert-Elliott (GE) channel, which is a special case of a large class of channels with hidden Markov memory. In a procedure referred to as estimation decoding, the sum-product algorithm (SPA) is used to perform LDPC decoding jointly with channel-state detection. Density evolution results show (and simulation results confirm) that such decoders provide a significantly enlarged region of successful decoding within the GE parameter space, compared with decoders that do not exploit the channel memory. By considering a variety of ways in which a GE channel may be degraded, it is shown how knowledge of the decoding behavior at a single point of the GE parameter space may be extended to a larger region within the space, thereby mitigating the large complexity needed in using density evolution to explore the parameter space point-by-point. Using the GE channel as a straightforward example, we conclude that analysis of estimation decoding for LDPC codes is feasible in channels with memory, and that such analysis shows large potential gains.