A Unified Discretization Approach to Compute-Forward: From Discrete to Continuous Inputs

TL;DR

This paper introduces a unified discretization method that extends compute-forward techniques from discrete to continuous channels, including Gaussian, using an algebraic generalization of Rénnyi's information dimension.

Contribution

It provides a unified framework for compute-forward across discrete and continuous channels through a novel discretization approach and algebraic generalization of information dimension.

Findings

Derived a simultaneous-decoding rate region for continuous channels

Unified discrete and continuous rate regions with a single expression

Extended compute-forward techniques to Gaussian channels

Abstract

Compute-forward is a coding technique that enables receiver(s) in a network to directly decode one or more linear combinations of the transmitted codewords. Initial efforts focused on Gaussian channels and derived achievable rate regions via nested lattice codes and single-user (lattice) decoding as well as sequential (lattice) decoding. Recently, these results have been generalized to discrete memoryless channels via nested linear codes and joint typicality coding, culminating in a simultaneous-decoding rate region for recovering one or more linear combinations from $K$ users. Using a discretization approach, this paper translates this result into a simultaneous-decoding rate region for a wide class of continuous memoryless channels, including the important special case of Gaussian channels. Additionally, this paper derives a single, unified expression for both discrete and continuous rate regions via an algebraic generalization of R\'enyi's information dimension.