LEO Satellite-Enabled Grant-Free Random Access with MIMO-OTFS

TL;DR

This paper proposes a novel joint channel estimation and device detection method for LEO satellite grant-free access systems using MIMO-OTFS, leveraging deep learning and EM algorithms to improve performance under high delay and Doppler shifts.

Contribution

It introduces a new parallel tensor estimation approach combined with deep learning and EM to enhance device activity detection in satellite communications.

Findings

Outperforms conventional methods in simulations

Effectively estimates channels with large delay and Doppler shifts

Improves device detection accuracy in LEO satellite systems

Abstract

This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppler shift. In addition, the multiple-input multiple-output (MIMO) with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link. To simplify the computation process, we estimate the channel tensor in parallel along the delay dimension. Then, the deep learning and expectation-maximization approach are integrated into the generalized approximate message passing with cross-correlation--based Gaussian prior to capture the channel sparsity in the delay-Doppler-angle domain and learn the hyperparameters. Finally, active devices are detected by computing energy of the estimated channel. Simulation results demonstrate that the proposed algorithms outperform conventional methods.