Managing Hardware Impairments in Hybrid Millimeter Wave MIMO Systems: A Dictionary Learning-based Approach

Abstract

Compressed sensing-based strategies have been derived in prior work to reduce training overhead when estimating the high dimensional millimeter wave MIMO channel. These techniques rely on a channel model based on a sparsifying dictionary which does not account for hardware impairments such as calibration errors, mutual coupling effects, or manufacturing errors in the inter-spacing between the array elements. In this paper, we propose a learning strategy for the sparsifying dictionary that considers a channel model with hardware impairments, embedding these effects into the dictionary itself. This way, a sparser representation of the channel can be obtained even when considering realistic implementations of the antenna array and the radio frequency chains. Numerical simulations with different system configurations and parameters of the hardware impairments, show the effectiveness of the proposed dictionary learning algorithm for channel estimation at millimeter wave frequencies with hybrid MIMO architectures.