Optimal Relay Functionality for SNR Maximization in Memoryless Relay Networks

TL;DR

This paper introduces a new SNR-optimal relay function called estimate and forward (EF) for memoryless relay networks, demonstrating its superior performance over traditional methods like amplify and forward (AF) and demodulate and forward (DF).

Contribution

The paper develops a generalized SNR framework for memoryless relay functions and introduces MMSUEE, establishing EF as the SNR-optimal relay scheme across various network configurations.

Findings

EF outperforms AF and DF in various relay network setups.

EF is near-optimal at high or low power depending on network type.

For Gaussian inputs, EF, AF, and DF are equivalent.

Abstract

We explore the SNR-optimal relay functionality in a \emph{memoryless} relay network, i.e. a network where, during each channel use, the signal transmitted by a relay depends only on the last received symbol at that relay. We develop a generalized notion of SNR for the class of memoryless relay functions. The solution to the generalized SNR optimization problem leads to the novel concept of minimum mean square uncorrelated error estimation(MMSUEE). For the elemental case of a single relay, we show that MMSUEE is the SNR-optimal memoryless relay function regardless of the source and relay transmit power, and the modulation scheme. This scheme, that we call estimate and forward (EF), is also shown to be SNR-optimal with PSK modulation in a parallel relay network. We demonstrate that EF performs better than the best of amplify and forward (AF) and demodulate and forward (DF), in both parallel and serial relay networks. We also determine that AF is near-optimal at low transmit power in a parallel network, while DF is near-optimal at high transmit power in a serial network. For hybrid networks that contain both serial and parallel elements, and when robust performance is desired, the advantage of EF over the best of AF and DF is found to be significant. Error probabilities are provided to substantiate the performance gain obtained through SNR optimality. We also show that, for \emph{Gaussian} inputs, AF, DF and EF become identical.