Performance analysis of linear codes under maximum-likelihood decoding: a tutorial
Communications and Information Theory
Construction of Universal Codes Using LDPC Matrices and Their Error Exponents
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
An Application of Linear Codes to the Problem of Source Coding with Partial Side Information
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
The capacity of finite Abelian group codes over symmetric memoryless channels
IEEE Transactions on Information Theory
Error exponent of exclusive-or multiple-access channels
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
Coset codes for compound multiple access channels with common information
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
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
Performance bounds for nonbinary linear block codes over memoryless symmetric channels
IEEE Transactions on Information Theory
Performance bounds for erasure, list and decision feedback schemes with linear block codes
IEEE Transactions on Information Theory
Capacity-achieving codes with bounded graphical complexity and maximum likelihood decoding
IEEE Transactions on Information Theory
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This work provides techniques to apply the channel coding theorem and the resulting error exponent, which was originally derived for totally random block-code ensembles, to ensembles of codes with less restrictive randomness demands. As an example, the random coding technique can even be applied for an ensemble that contains a single code. For a specific linear code, we get an upper bound for the error probability, which equals Gallager's (1968) random coding bound, up to a factor determined by the maximum ratio between the weight distribution of the code, and the expected random weight distribution