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

2025-08-26

AI Summary

This paper addresses the Goal-oriented Multiple Access (GoMA) problem in multi-agent wireless channel sharing, filling a critical gap in distributed multi-node coordinated scheduling. We propose the first distributed theoretical framework for goal-oriented multiple access, breaking the traditional paradigm of strict separation between communication and application layers by directly coupling channel access decisions to end-application performance objectives. Leveraging game-theoretic modeling, we characterize the inherent non-convexity and multiplicity of Nash equilibria in GoMA. We design a provably convergent distributed optimization algorithm and integrate bandit learning to enable adaptive policy evolution under no prior knowledge and limited feedback. Experiments demonstrate that our approach consistently outperforms centralized baselines across communication efficiency, task completion rate, and energy efficiency—achieving up to 100% improvement.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search
• Machine Learning: Online Learning & Bandits

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Designing distributed medium access for goal-oriented communication systems
• Analyzing non-convex optimization and Nash Equilibrium in multi-agent coordination
• Developing learning algorithms with limited feedback for wireless channel sharing

Innovation

• Distributed bandit learning for channel sharing
• Optimization approach reaching Nash Equilibrium
• Goal-oriented multiple access with limited feedback