Learning a Decentralized Medium Access Control Protocol for Shared Message Transmission
For IoT networks with massive device access, this work addresses a new challenge of coordinating shared message transmissions without inter-node communication, providing a practical solution for efficient medium access control.
This paper formalizes the multi-shared-message decentralized random access problem for IoT networks, where nodes must transmit shared messages to a central controller without inter-node communication or retransmissions. The proposed decentralized learning-based framework enables nodes to autonomously synthesize deterministic transmission strategies, achieving robust performance and fast adaptation to dynamic changes, outperforming state-of-the-art approaches.
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. To our knowledge, this is the first work to formalize and analyze the multi-shared-message decentralized random access problem. 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 existence of an optimal deterministic strategy, 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 state-of-the-art approaches.