Cooperative Relaying at Finite SNR -- Role of Quantize-Map-and-Forward

TL;DR

This paper develops an optimization framework for Quantize-Map-and-Forward relaying in slow fading channels, improving performance at moderate SNRs by optimizing quantization and relay scheduling based on available CSI.

Contribution

It introduces a universal quantizer for N-relay networks that reduces the approximation gap from (N) to (log(N)) bits/sec/Hz, and derives analytical solutions for specific network configurations.

Findings

Optimized QMF schemes outperform traditional methods at finite SNRs.

Universal quantizer reduces approximation gap from (N) to (log(N)) bits/sec/Hz.

Hybrid schemes combining QMF with Decode-Forward yield significant finite SNR gains.

Abstract

Quantize-Map-and-Forward (QMF) relaying has been shown to achieve the optimal diversity-multiplexing trade-off (DMT) for arbitrary slow fading full-duplex networks as well as for the single-relay half-duplex network. A key reason for this is that quantizing at the noise level suffices to achieve the cut-set bound approximately to within an additive gap, without any requirement of instantaneous channel state information (CSI). However, DMT only captures the high SNR performance and potentially, limited CSI at the relay can improve performance at moderate SNRs. In this work we propose an optimization framework for QMF relaying over slow fading channels. Focusing on vector Gaussian quantizers, we optimize the outage probability for the full-duplex and half-duplex single relay by finding the best quantization level and relay schedule according to the available CSI at the relays. For the N-relay diamond network, we derive an universal quantizer that sharpens the additive approximation gap of QMF from the conventional \Theta(N) bits/s/Hz to \Theta(log(N)) bits/s/Hz using only network topology information. Analytical solutions to channel-aware optimal quantizers for two-relay and symmetric N-relay diamond networks are also derived. In addition, we prove that suitable hybridizations of our optimized QMF schemes with Decode-Forward (DF) or Dynamic DF protocols provide significant finite SNR gains over the individual schemes.