Grant-Free Access: Machine Learning for Detection of Short Packets

TL;DR

This paper investigates machine learning techniques, specifically neural networks and random forests, as efficient methods for detecting short data packets in satellite IoT communications, outperforming traditional correlation-based methods.

Contribution

It introduces machine learning algorithms for packet detection in asynchronous satellite IoT scenarios, demonstrating significant performance improvements over traditional techniques.

Findings

ML algorithms outperform traditional correlation methods

High detection accuracy even with collisions

Potential for classifying interference levels

Abstract

In this paper, we explore the use of machine learning methods as an efficient alternative to correlation in performing packet detection. Targeting satellite-based massive machine type communications and internet of things scenarios, our focus is on a common channel shared among a large number of terminals via a fully asynchronous ALOHA protocol to attempt delivery of short data packets. In this setup, we test the performance of two algorithms, neural networks and random forest, which are shown to provide substantial improvements over {traditional} techniques. Excellent performance is demonstrated in terms of detection and false alarm probability also in the presence of collisions among user transmissions. The ability of machine learning to extract further information from incoming signals is also studied, discussing the possibility to classify detected preambles based on the level of interference they undergo.