# Learn2MAC: Online Learning Multiple Access for URLLC Applications

## TL;DR

This paper introduces Learn2MAC, an online learning-based distributed protocol for IoT devices that guarantees low latency and high throughput in uplink communications, outperforming traditional random access methods.

## Contribution

The paper proposes a novel fully distributed protocol using online exponentiated gradient algorithms for IoT uplink access, addressing latency guarantees absent in prior random access protocols.

## Key findings

- Achieves good latent throughput and low energy consumption.
- Outperforms baseline random access protocols significantly.
- Demonstrates effectiveness through simulation comparisons.

## 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.