Short Range 3D MIMO mmWave Channel Reconstruction via Geometry-aided AoA Estimation
In some millimeter wave (mmWave) applications, such as wearables, the distance between the transceivers is relatively short. Further, the channel has significant angular spread in both azimuth and elevation domains even in line-of-sight (LoS). Under such conditions, hybrid mmWave architectures with multiple analog uniform planar arrays (UPAs) potentially allow spatial multiplexing even in LoS provided that the high rank structure of the channel is captured. The conventional far-field channel estimation methods are not generally suitable for these scenarios and perform poorly. We consider parametrized spatial channel estimation, where the known antenna array geometry is exploited to recover the angle-of-arrivals (AoAs) of the 3D multiple-input multiple-output (MIMO) channel. The channel is then reconstructed using these AoA estimates and the known geometry. We show that conventional maximum a posteriori (MAP) estimation of the channel parameters suffers from high computational complexity and may not be not applicable for low powered devices. To this end, we propose a lower complexity message passing algorithm for short range channel estimation. We show, by numerical examples, that the proposed technique achieves good performance with fewer pilot resources when compared to compressed sensing or antenna specific pilot based channel estimation.