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Open AccessJournal ArticleDOI

Low-Density Lattice Codes

TLDR
Low-density lattice codes (LDLC) are novel lattice code that can be decoded efficiently and approach the capacity of the additive white Gaussian noise (AWGN) channel and the paper discusses convergence results and implementation considerations.
Abstract
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.

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Proceedings ArticleDOI

Generalized approximate message passing for estimation with random linear mixing

TL;DR: In this article, the generalized AMP (G-AMP) algorithm is proposed to estimate a random vector observed through a linear transform followed by a componentwise probabilistic measurement channel.
Posted Content

Bayesian Compressive Sensing via Belief Propagation

TL;DR: This work performs asymptotically optimal Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model, and focuses on a two-state mixture Gaussian model that is easily adapted to other signal models.
Journal ArticleDOI

Bayesian Compressive Sensing Via Belief Propagation

TL;DR: In this paper, a two-state mixture Gaussian model is used to perform asymptotically optimal Bayesian inference using belief propagation decoding, which represents the CS encoding matrix as a graphical model.
Journal ArticleDOI

Nonparametric belief propagation

TL;DR: This work describes an extension of BP to continuous variable models, generalizing particle filtering, and Gaussian mixture filtering techniques for time series to more complex models and illustrates the power of the resulting nonparametric BP algorithm via two applications: kinematic tracking of visual motion and distributed localization in sensor networks.
Journal ArticleDOI

An Algebraic Approach to Physical-Layer Network Coding

TL;DR: A general framework is developed for studying nested-lattice-based PNC schemes-called lattice network coding (LNC) schemes for short-by making a direct connection between C&F and module theory and several generalized constructions of LNC schemes are given.
References
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Journal ArticleDOI

A mathematical theory of communication

TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Book

Probability, random variables and stochastic processes

TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.
Book

Iterative Methods for Sparse Linear Systems

Yousef Saad
TL;DR: This chapter discusses methods related to the normal equations of linear algebra, and some of the techniques used in this chapter were derived from previous chapters of this book.
Book

Probability, random variables, and stochastic processes

TL;DR: In this paper, the meaning of probability and random variables are discussed, as well as the axioms of probability, and the concept of a random variable and repeated trials are discussed.
Book

Low-Density Parity-Check Codes

TL;DR: A simple but nonoptimum decoding scheme operating directly from the channel a posteriori probabilities is described and the probability of error using this decoder on a binary symmetric channel is shown to decrease at least exponentially with a root of the block length.
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