Energy Efficient Cooperative Strategies for Relay-Assisted Downlink Cellular Systems Part II: Practical Design

TL;DR

This paper presents a practical design for energy-efficient relay-assisted downlink cellular systems, analyzing the impact of relay cognition on energy consumption and rate improvements through explicit formulas and simulations.

Contribution

It provides a detailed practical example with closed-form expressions for energy-rate trade-offs and introduces a numerical simulation setup for optimizing strategies in larger networks.

Findings

Relay cooperation significantly reduces energy consumption.

Cognition at relays enhances achievable rates.

Simulation results favor cooperative strategies over uncoordinated ones.

Abstract

In a companion paper [1], we present a general approach to evaluate the impact of cognition in a downlink cellular system in which multiple relays assist the transmission of the base station. This approach is based on a novel theoretical tool which produces transmission schemes involving rate-splitting, superposition coding and interference decoding for a network with any number of relays and receivers. This second part focuses on a practical design example for a network in which a base station transmits to three receivers with the aid of two relay nodes. For this simple network, we explicitly evaluate the impact of relay cognition and precisely characterize the trade offs between the total energy consumption and the rate improvements provided by relay cooperation. These closedform expressions provide important insights on the role of cognition in larger networks and highlights interesting interference management strategies. We also present a numerical simulation setup in which we fully automate the derivation of achievable rate region for a general relay-assisted downlink cellular network. Our simulations clearly show the great advantages provided by cooperative strategies at the relays as compared to the uncoordinated scenario under varying channel conditions and target rates. These results are obtained by considering a large number of transmission strategies for different levels of relay cognition and numerically determining one that is the most energy efficient. The limited computational complexity of the numerical evaluations makes this approach suitable for the optimization of transmission strategies for larger networks.