Iterative Merging Algorithm for Cooperative Data Exchange

TL;DR

This paper introduces an iterative merging algorithm to efficiently find minimum sum-rate strategies in cooperative data exchange systems without packet-splitting, improving computational complexity for up to 94 clients.

Contribution

The paper presents a novel iterative merging algorithm that recursively combines client sets to determine minimum sum-rate strategies more efficiently than existing methods.

Findings

The IM algorithm reduces complexity compared to previous algorithms for fewer than 94 clients.

It effectively learns minimum sum-rate strategies through local recovery rate allocation.

Experimental results demonstrate improved efficiency in cooperative data exchange scenarios.

Abstract

We consider the problem of finding the minimum sum-rate strategy in cooperative data exchange systems that do not allow packet-splitting (NPS-CDE). In an NPS-CDE system, there are a number of geographically close cooperative clients who send packets to help the others recover a packet set. A minimum sum-rate strategy is the strategy that achieves universal recovery (the situation when all the clients recover the whole packet set) with the the minimal sum-rate (the total number of transmissions). We propose an iterative merging (IM) algorithm that recursively merges client sets based on a lower estimate of the minimum sum-rate and updates to the value of the minimum sum-rate. We also show that a minimum sum-rate strategy can be learned by allocating rates for the local recovery in each merged client set in the IM algorithm. We run an experiment to show that the complexity of the IM algorithm is lower than that of the existing deterministic algorithm when the number of clients is lower than $94$.