Learning a Decentralized Medium Access Control Protocol for Shared Message Transmission

TL;DR

This paper introduces a decentralized learning framework for MAC in large IoT networks, enabling nodes to autonomously optimize message transmission strategies without inter-node communication, ensuring reliable delivery in dynamic environments.

Contribution

It presents a novel decentralized learning approach for MAC in IoT networks, with theoretical guarantees and adaptive online mechanisms for dynamic message transmission scenarios.

Findings

Framework outperforms existing bandit approaches in simulations.

Achieves high success rates in dynamic and large-scale networks.

Demonstrates robustness and scalability across various scenarios.

Abstract

In large-scale Internet of things networks, efficient medium access control (MAC) is critical due to the growing number of devices competing for limited communication resources. In this work, we consider a new challenge in which a set of nodes must transmit a set of shared messages to a central controller, without inter-node communication or retransmissions. Messages are distributed among random subsets of nodes, which must implicitly coordinate their transmissions over shared communication opportunities. The objective is to guarantee the delivery of all shared messages, regardless of which nodes transmit them. We first prove the optimality of deterministic strategies, and characterize the success rate degradation of a deterministic strategy under dynamic message-transmission patterns. To solve this problem, we propose a decentralized learning-based framework that enables nodes to autonomously synthesize deterministic transmission strategies aiming to maximize message delivery success, together with an online adaptation mechanism that maintains stable performance in dynamic scenarios. Extensive simulations validate the framework's effectiveness, scalability, and adaptability, demonstrating its robustness to varying network sizes and fast adaptation to dynamic changes in transmission patterns, outperforming existing multi-armed bandit approaches.