Hardware Impairments Aware Bayesian Learning Channel Estimator for mmWave Wireless Systems

We estimate the uplink channel in a multi-user millimeter wave system, which uses a hybrid architecture with low-resolution analog-to-digital converters and low-quality hardware-impaired radio frequency (RF) chains. We propose a hardware-impairment-aware correlated sparse Bayesian learning (HA-CSBL) channel estimation algorithm, which exploits the channel structured sparsity and its correlation by constructing a novel Gaussian prior model. The HA-CSBL algorithm also estimates the statistics of hardware impairments to mitigate their effect. We show that the HA-CSBL algorithm provides i) a lower normalized mean squared error (NMSE) than the existing algorithms in different practical sparsity scenarios. These improvements are due to the better prior design, and the hardware-impairment-awareness of the HA-CSBL algorithm.