Learning a Decentralized Medium Access Control Protocol for Shared Message Transmission

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.

For the pre-print version of the paper, please visit: https://arxiv.org/abs/2511.06001

The repository is structured as follow:

complementary

It contains code to generate data.

cond_data

It contains data for Conditional Activation Patterns.

data

It contains data for General Activation Patterns.

images

It contains figures produced for the paper.

my_utils

It contains utility functions.

results

It contains numerical results of simulations.

simple-inference

It contains scripts for simple inference procedures (without online training).

statistics

It contains scripts for computing statistics on the learned policies.

test

It contains scripts for plotting numerical results.

train

It contains training scripts.

If you find this work useful in your research, please consider citing:

@misc{amorosa2025learningdecentralizedmediumaccess,
      title={Learning a Decentralized Medium Access Control Protocol for Shared Message Transmission}, 
      author={Lorenzo Mario Amorosa and Zhan Gao and Roberto Verdone and Petar Popovski and Deniz Gündüz},
      year={2025},
      eprint={2511.06001},
      archivePrefix={arXiv},
      primaryClass={cs.NI},
      url={https://arxiv.org/abs/2511.06001}, 
}