This paper considers the joint channel estimation and device activity detection in the grant-free random access systems, where a large number of Internet-of-Things devices intend to communicate with a low-earth orbit satellite in a sporadic way. In addition, the massive multiple-input multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation is adopted to combat the dynamics of the terrestrial-satellite link. We first analyze the input-output relationship of the single-input single-output OTFS when the large delay and Doppler shift both exist, and then extend it to the grant-free random access with massive MIMO-OTFS. Next, by exploring the sparsity of channel in the delay-Doppler-angle domain, a two-dimensional pattern coupled hierarchical prior with the sparse Bayesian learning and covariance-free method (TDSBL-FM) is developed for the channel estimation. Then, the active devices are detected by computing the energy of the estimated channel. Finally, the generalized approximate message passing algorithm combined with the sparse Bayesian learning and two-dimensional convolution (ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-FM algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

中文翻译：

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LEO 卫星通信中使用大规模 MIMO-OTFS 的随机接入

本文考虑了无授权随机接入系统中的联合信道估计和设备活动检测，其中大量物联网设备打算以零星的方式与低地球轨道卫星通信。此外，采用正交时频空间（OTFS）调制的大规模多输入多输出（MIMO）来对抗地面卫星链路的动态。我们首先分析了大延迟和多普勒频移同时存在时单输入单输出OTFS的输入输出关系，然后将其扩展到大规模MIMO-OTFS的免授权随机接入。接下来，通过探索延迟多普勒角域中信道的稀疏性，为信道估计开发了一种二维模式耦合分层先验与稀疏贝叶斯学习和无协方差方法（TDSBL-FM）。然后，通过计算估计信道的能量来检测有源设备。最后，提出了结合稀疏贝叶斯学习和二维卷积的广义近似消息传递算法（ConvSBL-GAMP），以减少TDSBL-FM算法的计算量。仿真结果表明，所提出的算法优于传统方法。为了减少TDSBL-FM算法的计算量，提出了结合稀疏贝叶斯学习和二维卷积的广义近似消息传递算法（ConvSBL-GAMP）。仿真结果表明，所提出的算法优于传统方法。为了减少TDSBL-FM算法的计算量，提出了结合稀疏贝叶斯学习和二维卷积的广义近似消息传递算法（ConvSBL-GAMP）。仿真结果表明，所提出的算法优于传统方法。