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-CF) 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-CF algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.
翻译:本文审议了无赠与随机访问系统中联合频道估计和装置活动探测的情况,在这种系统中,大量互联网连接装置打算以零星的方式与低地球轨道卫星进行通信,此外,还采用了与正方位时频空间(OTFS)的大规模多投入多输出(MIMO)调制,以对抗地面卫星链接的动态。我们首先分析单输入单输出单输出OTFS的输入输出关系,当大型延迟和多普勒转换同时存在时,然后将其扩大到与大型MIMO-OTFS的无偿访问。接着,通过探索延迟-多普勒角域频道的广度多输出(MIMO)和正位时位时频时频频率空间(MIMO-OTFS)多输出(MIMO)多输出多输出(MIMO)(MIMO)多输出(MIMO)(MIMO)(MIMO)(MIMO)(MIL-CFCFC)(ML-CFSL-CFSL) (DAL) (DLAVAL-SLMLADL) (DLML) (SLAD) (SLAVALVOLOLOLOLA) (SL) (SLA) (SL) (SL) (SL) (SL) (SLAD) (SL) (SV) (SL) (D) (SL) (SL) (T) (T) (后代算算法) 。