Elements of information theory
Elements of information theory
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Single-Gaussian messages and noise thresholds for decoding low-density lattice codes
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Efficient parametric decoder of low density lattice codes
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Universal bound on the performance of lattice codes
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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This letter describes a belief-propagation decoder for low-density lattice codes of finite dimension, in which the messages are represented as single Gaussian functions. Compared to previously-proposed decoders, memory is reduced because each message consists of only two values, the mean and variance. Complexity is also reduced because the check node operations are on single Gaussians, avoiding approximations needed previously, and because the variable node performs approximations on a smaller number of Gaussians. For lattice dimension n =1000 and 10,000, this decoder looses no more than 0.1 dB in SNR, compared to the decoders which use much more memory.