Throughput Optimized Multi-Source Cooperative Networks With Compute-and-Forward

TL;DR

This paper explores a compute-and-forward relaying strategy in multi-source multicast networks with multiple relays, optimizing throughput under delay constraints and demonstrating its advantages over traditional methods through theoretical analysis and simulations.

Contribution

It introduces a novel compute-and-forward protocol for multi-relay networks, including methods to optimize network coefficients and resource allocation for maximum throughput.

Findings

CPF outperforms decode-and-forward in throughput.

More relays enhance CPF performance due to increased diversity.

Local optimal methods achieve near-global optimal performance in large networks.

Abstract

In this work, we investigate a multi-source multi-cast network with the aid of an arbitrary number of relays, where it is assumed that no direct link is available at each S-D pair. The aim is to find the fundamental limit on the maximal common multicast throughput of all source nodes if resource allocations are available. A transmission protocol employing the relaying strategy, namely, compute-and-forward (CPF), is proposed. {We also adjust the methods in the literature to obtain the integer network-constructed coefficient matrix (a naive method, a local optimal method as well as a global optimal method) to fit for the general topology with an arbitrary number of relays. Two transmission scenarios are addressed. The first scenario is delay-stringent transmission where each message must be delivered within one slot. The second scenario is delay-tolerant transmission where no delay constraint is imposed. The associated optimization problems to maximize the short-term and long-term common multicast throughput are formulated and solved, and the optimal allocation of power and time slots are presented. Performance comparisons show that the CPF strategy outperforms conventional decode-and-forward (DF) strategy. It is also shown that with more relays, the CPF strategy performs even better due to the increased diversity. Finally, by simulation, it is observed that for a large network in relatively high SNR regime, CPF with the local optimal method for the network-constructed matrix can perform close to that with the global optimal method.