Reducing Reconciliation Communication Cost with Compressed Sensing

TL;DR

This paper introduces a novel reconciliation method using compressive sensing that reduces communication costs without needing prior knowledge of set differences, supported by theoretical and simulation results.

Contribution

It presents a new reconciliation approach leveraging compressive sensing to eliminate the requirement of prior set difference size knowledge.

Findings

Achieves comparable efficiency to existing methods

Does not require prior knowledge of set differences

Validated through theoretical analysis and simulations

Abstract

We consider a reconciliation problem, where two hosts wish to synchronize their respective sets. Efficient solutions for minimizing the communication cost between the two hosts have been previously proposed in the literature. However, they rely on prior knowledge about the size of the set differences between the two sets to be reconciled. In this paper, we propose a method which can achieve comparable efficiency without assuming this prior knowledge. Our method uses compressive sensing techniques which can leverage the expected sparsity in set differences. We study the performance of the method via theoretical analysis and numerical simulations.