Low-Complexity Near-Optimal Codes for Gaussian Relay Networks

TL;DR

This paper introduces computationally efficient network codes for Gaussian relay networks that nearly achieve the theoretical capacity, building on previous work but with improved practicality.

Contribution

The paper presents a concatenated coding scheme that uses existing codes as inner codes, achieving near-capacity performance with low complexity.

Findings

Codes achieve a constant gap from capacity

The proposed codes are computationally tractable

Builds on and improves previous capacity-approaching codes

Abstract

We consider the problem of information flow over Gaussian relay networks. Similar to the recent work by Avestimehr \emph{et al.} [1], we propose network codes that achieve up to a constant gap from the capacity of such networks. However, our proposed codes are also computationally tractable. Our main technique is to use the codes of Avestimehr \emph{et al.} as inner codes in a concatenated coding scheme.