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
Power-constrained communications using LDLC lattices
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
A low density lattice decoder via non-parametric belief propagation
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Bayesian compressive sensing via belief propagation
IEEE Transactions on Signal Processing
Nonparametric belief propagation
Communications of the ACM
Reduced-memory decoding of low-density lattice codes
IEEE Communications Letters
| Hi-index | 754.85 |
Low-density lattice codes (LDLC) are novel lattice codes that can be decoded efficiently and approach the capacity of the additive white Gaussian noise (AWGN) channel. In LDLC a codeword x is generated directly at the n-dimensional Euclidean space as a linear transformation of a corresponding integer message vector b, i.e., x = Gb-1, where H = G-1 is restricted to be sparse. The fact that H is sparse is utilized to develop a linear-time iterative decoding scheme which attains, as demonstrated by simulations, good error performance within ~0.5 dB from capacity at block length of n =100,000 symbols. The paper also discusses convergence results and implementation considerations.