An Efficient Millimeter-Wave MIMO Channel Estimation Scheme for Space Information Networks

In this paper, we establish a sparse geometric-based millimeter-wave (mmWave) band multiple-input and multiple-output (MIMO) channel model between a high throughput satellite (HTS) and terrestrial user equipments (UEs) for space information network (SIN). By exploiting the inherent sparsity of mmWave band, we propose an adaptive random-selected multi-beamforming (ARM) estimation scheme for efficient mmWave MIMO channel modeling in SIN. The ARM estimation scheme measures the propagation paths between the HTS and UEs in angle domain, where the HTS can randomly select multiple beamformings to estimate the CSI of multiple UEs simultaneously. Compare to the existing fix number of measurements schemes, the required number of measurements in our ARM estimation scheme can adaptively reduce as well as the signal-to-noise ratio (SNR) increases. Simulation results show that our ARM estimation scheme can reduce the required number of measurements and achieve a better tracking performance over a wide range of SNRs.