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Lattice codes for the Gaussian relay channel: Decode-and-Forward and Compress-and-Forward

TLDR
The results suggest that structured/lattice codes may be used to mimic, and sometimes outperform, random Gaussian codes in general Gaussian networks.
Abstract
Lattice codes are known to achieve capacity in the Gaussian point-to-point channel, achieving the same rates as independent, identically distributed (i.i.d.) random Gaussian codebooks. Lattice codes are also known to outperform random codes for certain channel models that are able to exploit their linearity. In this work, we show that lattice codes may be used to achieve the same performance as known i.i.d. Gaussian random coding techniques for the Gaussian relay channel, and show several examples of how this may be combined with the linearity of lattices codes in multi-source relay networks. In particular, we present a nested lattice list decoding technique, by which, lattice codes are shown to achieve the Decode-and-Forward (DF) rate of single source, single destination Gaussian relay channels with one or more relays. We next present two examples of how this DF scheme may be combined with the linearity of lattice codes to achieve new rate regions which for some channel conditions outperform analogous known Gaussian random coding techniques in multi-source relay channels. That is, we derive a new achievable rate region for the two-way relay channel with direct links and compare it to existing schemes, and derive another achievable rate region for the multiple access relay channel. We furthermore present a lattice Compress-and-Forward (CF) scheme for the Gaussian relay channel which exploits a lattice Wyner-Ziv binning scheme and achieves the same rate as the Cover-El Gamal CF rate evaluated for Gaussian random codes. These results suggest that structured/lattice codes may be used to mimic, and sometimes outperform, random Gaussian codes in general Gaussian networks.

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Citations
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Book

Lattice Coding for Signals and Networks: A Structured Coding Approach to Quantization, Modulation and Multiuser Information Theory

TL;DR: It is shown how high dimensional lattice codes can close the gap to the optimal information theoretic solution, including the characterisation of error exponents, when generalising the framework to Gaussian networks.
Journal ArticleDOI

Expanding the Compute-and-Forward Framework: Unequal Powers, Signal Levels, and Multiple Linear Combinations

TL;DR: An expanded compute-and-forward framework is proposed that incorporates both the advantages of employing unequal powers at the transmitters and decoding more than one linear combination at each receiver and permits an intuitive interpretation in terms of signal levels.
Book

Multi-way Communications: An Information Theoretic Perspective

TL;DR: Fundamental limits on the achievable rates are reviewed, and making use of a linear high-SNR deterministic channelmodel to provide valuable insights which are helpful when discussing the coding schemes for Gaussian channel models in detail are discussed.
Journal ArticleDOI

Compute-and-Forward on a Multiaccess Relay Channel: Coding and Symmetric-Rate Optimization

TL;DR: In this article, the authors considered a half-duplex relay network where the destination decodes two integer-valued linear combinations that relate the transmitted codewords, and then computes the two linear combinations locally.
Posted Content

A Joint Typicality Approach to Algebraic Network Information Theory.

TL;DR: A joint typicality framework for encoding and decoding nested linear codes for multi-user networks is presented, which establishes an achievable rate region for computing the weighted sum of nested linear codewords over a discrete memoryless multiple-access channel (MAC).
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