Learn2MAC: Online Learning Multiple Access for URLLC Applications

Apostolos Destounis; Dimitrios Tsilimantos; Mérouane Debbah; Georgios S. Paschos

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arXiv · 1904.00665

Learn2MAC: Online Learning Multiple Access for URLLC Applications

Apostolos Destounis·Dimitrios Tsilimantos·Mérouane Debbah·Georgios S. Paschos

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Abstract

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 $p^i$, 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.

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BibTeX RIS

Apostolos Destounis, Dimitrios Tsilimantos, Mérouane Debbah, Georgios S. Paschos. 2019-04-01. Learn2MAC: Online Learning Multiple Access for URLLC Applications. https://arxiv.org/abs/1904.00665

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