Optimal Relay-Subset Selection and Time-Allocation in Decode-and-Forward Cooperative Networks

TL;DR

This paper introduces an optimal method for relay subset selection and time allocation in decode-and-forward cooperative networks, enhancing network performance and computational efficiency.

Contribution

It presents a novel optimal resource allocation framework and a recursive algorithm for relay selection in cooperative networks, improving efficiency and performance.

Findings

Adding relays benefits small networks more than large ones.

Optimized resource allocation increases active relays over wider SNR ranges.

Linear network constellations outperform grid constellations.

Abstract

We present the optimal relay-subset selection and transmission-time for a decode-and-forward, half-duplex cooperative network of arbitrary size. The resource allocation is obtained by maximizing over the rates obtained for each possible subset of active relays, and the unique time allocation for each set can be obtained by solving a linear system of equations. We also present a simple recursive algorithm for the optimization problem which reduces the computational load of finding the required matrix inverses, and reduces the number of required iterations. Our results, in terms of outage rate, confirm the benefit of adding potential relays to a small network and the diminishing marginal returns for a larger network. We also show that optimizing over the channel resources ensures that more relays are active over a larger SNR range, and that linear network constellations significantly outperform grid constellations. Through simulations, the optimization is shown to be robust to node numbering.