Upper Bounds on the Capacity of 2-Layer $N$-Relay Symmetric Gaussian Network

TL;DR

This paper derives upper bounds on the capacity of symmetric 2-layer N-relay Gaussian networks, showing that joint optimization over correlation coefficients is unnecessary for tighter bounds.

Contribution

It extends capacity upper bounds to multi-layer symmetric relay networks and simplifies the analysis by removing the need for joint correlation optimization.

Findings

Upper bounds for 2-layer N-relay Gaussian networks are established.

Joint optimization over correlation coefficients is unnecessary for these bounds.

The results improve understanding of capacity limits in layered relay networks.

Abstract

The Gaussian parallel relay network, in which two parallel relays assist a source to convey information to a destination, was introduced by Schein and Gallager. An upper bound on the capacity can be obtained by considering broadcast cut between the source and relays and multiple access cut between relays and the destination. Niesen and Diggavi derived an upper bound for Gaussian parallel $N$-relay network by considering all other possible cuts and showed an achievability scheme that can attain rates close to the upper bound in different channel gain regimes thus establishing approximate capacity. In this paper we consider symmetric layered Gaussian relay networks in which there can be many layers of parallel relays. The channel gains for the channels between two adjacent layers are symmetrical (identical). Relays in each layer broadcast information to the relays in the next layer. For 2-layer $N$-relay Gaussian network we give upper bounds on the capacity. Our analysis reveals that for the upper bounds, joint optimization over correlation coefficients is not necessary for obtaining stronger results.