A Theory of Goal-Oriented Medium Access: Protocol Design and Distributed Bandit Learning

TL;DR

This paper introduces a theoretical framework for goal-oriented distributed medium access, demonstrating that decentralized strategies can outperform centralized ones in wireless communication by optimizing coordination among multiple agents.

Contribution

It develops a novel theoretical analysis of distributed goal-oriented medium access, characterizes Nash equilibria, and proposes a learning algorithm that improves performance and energy efficiency.

Findings

Distributed approach outperforms centralized methods by up to 100%.

The problem admits multiple Nash Equilibria, with a characterization of best responses.

A limited-feedback learning algorithm effectively coordinates agents without prior knowledge.

Abstract

The Goal-oriented Communication (GoC) paradigm breaks the separation between communication and the content of the data, tailoring communication decisions to the specific needs of the receiver and targeting application performance. While recent studies show impressive encoding performance in point-to-point scenarios, the multi-node distributed scenario is still almost unexplored. Moreover, the few studies to investigate this consider a centralized collision-free approach, where a central scheduler decides the transmission order of the nodes. In this work, we address the Goal-oriented Multiple Access (GoMA) problem, in which multiple intelligent agents must coordinate to share a wireless channel and avoid mutual interference. We propose a theoretical framework for the analysis and optimization of distributed GoMA, serving as a first step towards its complete characterization. We prove that the problem is non-convex and may admit multiple Nash Equilibrium (NE) solutions. We provide a characterization of each node's best response to others' strategies and propose an optimization approach that provably reaches one such NE, outperforming centralized approaches by up to 100% while also reducing energy consumption. We also design a distributed learning algorithm that operates with limited feedback and no prior knowledge.