A Sparse Bayesian Learning Method of Joint Activity Detection and Channel Estimation for LEO Grant-Free Random Access

The low earth orbit (LEO) satellite communication network has attracted extensive attentions owing to its advantages of seamless coverage and low propagation delay, which provides a promising solution to realize massive access for Internet-of-Things (IoT) devices. In this paper, we investigate the problem of joint activity detection and channel estimation (JADCE) in the uplink multi-input multi-output (MIMO) LEO satellite communication system based on the grant free random access (GF-RA) scheme, owing to the effectiveness of the GF-RA scheme for massive machine type communications. Considering the sporadic traffic of IoT devices, the problem can be solved via compressive sensing (CS) technology. To facilitate cost-effective hardware implementation, we utilize the Toeplitz matrix to generate the preamble. A multiple measurement vector algorithm based on sparse bayesian learning is developed for performing active device detection and channel estimation by fully exploiting the sparsity of the device state matrix. Simulation results indicate that the proposed method achieves lower device activity detection error probability and better channel estimation performance than the baseline method in the literature.