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.
@article{arxiv.2208.01828,
title = {LEO Satellite-Enabled Grant-Free Random Access with MIMO-OTFS},
author = {Boxiao Shen and Yongpeng Wu and Wenjun Zhang and Geoffrey Ye Li and Jianping An and Chengwen Xing},
journal= {arXiv preprint arXiv:2208.01828},
year = {2022}
}
Comments
This paper has been accepted for presentation at the IEEE GLOBECOM 2022. arXiv admin note: text overlap with arXiv:2202.13058