摘要
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.
源语言 | 英语 |
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页(从-至) | 3308-3313 |
页数 | 6 |
期刊 | Proceedings - IEEE Global Communications Conference, GLOBECOM |
DOI | |
出版状态 | 已出版 - 2022 |
活动 | 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, 巴西 期限: 4 12月 2022 → 8 12月 2022 |