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.
翻译:本文调查低地轨道卫星无赠与随机访问系统的联合频道估计和装置活动探测,有巨大的差异性延迟和多普勒转换;此外,利用对流时频空间调制(OTFS)的多投入多输出数据(MIMO)来对抗地面卫星链接的动态;为简化计算过程,我们在延迟方面同时估算了信道的振动值;然后,将深度学习和预期-最大化方法纳入了与以交叉关系为基础的高斯传递的通用近似信息中,然后在延迟-多普勒角域捕捉频道的频度并学习超参数;最后,通过计算估计通道的能量,检测了主动装置;模拟结果显示,拟议的算法超越了常规方法。