Channel estimation for mMIMO LEO satellite Internet-of-Things uplink transmission

In this paper, we investigate the low complexity uplink transmission channel estimation (CE) algorithms for the problem of limited computational resources in massive multiple-input multiple-output (mMIMO) low Earth orbit (LEO) satellite Internet of Things (IoT) scenarios. We use angular-domain channel to characterize uplink transmission channel to exploit its sparse characteristics in the angular-domain and formulate the joint user activity detection (UAD) and CE problem as a sparse vector recovery problem by combining it with the sparse characteristics of the active user matrices in the satellite IoT scenario. Finally, we propose the enhanced simultaneous orthogonal matching pursuit (ESOMP) algorithm based on the theory of compressive sensing (CS), and verify it through the simulation experiment. The simulation results show that ESOMP algorithm can reduce the computational complexity while guaranteeing the algorithm's performance.