Dithered Beamforming for Channel Estimation in Mmwave-Based Massive Mimo
Evangelos Vlachos, John Thompson · 2018
In this work we consider the challenging problem of channel estimation at the receiver of a massive multiple-input multiple-output system with hybrid analog/digital beamforming and low-resolution quantization. We propose a dithered beamforming architecture, where random control signals are injected to the analog part of the receiver beamformer and to the analog-to-digital converters to introduce randomness into the signal capturing process and combat the stair-case quantization effects. The statistical properties of the dithered output are captured via an Expectation-Maximization approximation of the maximum a-posteriori estimator. A low-complexity algorithm is proposed which exhibits performance close to the oracle-based least-squares estimation of the sparse channel.