Random Access for LEO Satellite Communication Systems via Deep Learning

TL;DR

This paper presents a deep learning-based random access framework for LEO satellite communication systems that improves access success, reduces delay, and enhances resource utilization by early collision detection and opportunistic transmission.

Contribution

It introduces a novel deep learning framework with an early collision classifier and opportunistic scheme tailored for LEO SatCom, addressing propagation delays and Doppler shifts.

Findings

Higher access success probability in simulations

Lower delay and better channel utilization

Reduced computational complexity

Abstract

Integrating contention-based random access procedures into low Earth orbit (LEO) satellite communication (SatCom) systems poses new challenges, including long propagation delays, large Doppler shifts, and a large number of simultaneous access attempts. These factors degrade the efficiency and responsiveness of conventional random access schemes, particularly in scenarios such as satellite-based internet of things and direct-to-device services. In this paper, we propose a deep learning-based random access framework designed for LEO SatCom systems. The framework incorporates an early preamble collision classifier that uses multi-antenna correlation features and a lightweight 1D convolutional neural network to estimate the number of collided users at the earliest stage. Based on this estimate, we introduce an opportunistic transmission scheme that balances access probability and resource efficiency to improve success rates and reduce delay. Simulation results under 3GPP-compliant LEO settings confirm that the proposed framework achieves higher access success probability, lower delay, better physical uplink shared channel utilization, and reduced computational complexity compared to existing schemes.