This paper addresses a fundamental limitation of previous random access protocols, their lack of latency performance guarantees. We consider K IoT transmitters competing for uplink resources and we design a fully distributed protocol for deciding how they access the medium. Specifically, each transmitter restricts decisions to a locally-generated dictionary of transmission patterns. At the beginning of a frame, pattern i is chosen with probability pi, and an online exponentiated gradient algorithm is used to adjust this probability distribution. The performance of the proposed scheme is showcased in simulations, where it is compared with a baseline random access protocol. Simulation results show that (a) the proposed scheme achieves good latent throughput performance and low energy consumption, while (b) it outperforms by a big margin random transmissions.
@article{arxiv.1904.00665,
title = {Learn2MAC: Online Learning Multiple Access for URLLC Applications},
author = {Apostolos Destounis and Dimitrios Tsilimantos and Mérouane Debbah and Georgios S. Paschos},
journal= {arXiv preprint arXiv:1904.00665},
year = {2019}
}