ARM: Adaptive Random-Selected Multi-Beamforming Estimation Scheme for Satellite-Based Internet of Things

In this paper, we focus on the millimeter-wave (mmWave) band multiple-input and multiple-output (MIMO) channel estimation in the satellite-based Internet of Things (S-IoT). At first, we establish a sparse geometric-based mmWave MIMO channel model between a high throughput satellite (HTS) and multiple terrestrial user equipments (UEs) for the S-IoT downlink system. By exploiting the sparsity inherent to the mmWave band channel, we propose an efficient adaptive random-selected multi-beamforming (ARM) estimation scheme, which can simultaneously estimate the mmWave MIMO channel state information (CSI) for multiple UEs in angle domain. The ARM estimation scheme measures of the CSI between the HTS and multiple UEs by utilizing a series of random combinations of transmitting beamforming vectors and receiving UEs' antennas and the required number of measurements can adaptively reduce as well as the signal-to-noise ratios (SNR) increases. To further improve the performance of ARM estimation scheme, the number of each beamforming been selected for the channel measurement is buffered, and modify the probability of the random selection of beams and UEs. Two improved ARM estimation schemes are proposed, one is ARM forcing adaptation beam selection (ARM-FABS) scheme, and the other is ARM partially estimated beam selection (ARM-PEBS) scheme. The simulation results show that the proposed ARM estimation schemes can reduce the required number of measurements and achieve a better tracking performance over a wide range of SNRs.