On Maximizing Coverage in Gaussian Relay Networks

TL;DR

This paper explores how to optimize relay placement in Gaussian relay networks to maximize geographic coverage at a fixed rate, comparing decode-and-forward and compress-and-forward strategies under various conditions.

Contribution

It introduces a new coverage-based optimization framework for relay deployment and analyzes the performance of DF and CF strategies within this context.

Findings

DF provides better coverage than CF when decoding is possible.

Coverage regions are sensitive to relay location and path loss, especially for DF.

CF degrades more gracefully under changing channel conditions.

Abstract

Results for Gaussian relay channels typically focus on maximizing transmission rates for given locations of the source, relay and destination. We introduce an alternative perspective, where the objective is maximizing coverage for a given rate. The new objective captures the problem of how to deploy relays to provide a given level of service to a particular geographic area, where the relay locations become a design parameter that can be optimized. We evaluate the decode and forward (DF) and compress and forward (CF) strategies for the relay channel with respect to the new objective of maximizing coverage. When the objective is maximizing rate, different locations of the destination favor different strategies. When the objective is coverage for a given rate, and the relay is able to decode, DF is uniformly superior in that it provides coverage at any point served by CF. When the channel model is modified to include random fading, we show that the monotone ordering of coverage regions is not always maintained. While the coverage provided by DF is sensitive to changes in the location of the relay and the path loss exponent, CF exhibits a more graceful degradation with respect to such changes. The techniques used to approximate coverage regions are new and may be of independent interest.