Hybrid mmWave MIMO Systems under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-aided Configuration

Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated discrete angles of arrival/departure. In addition, these assumed to be the same all subcarriers, without considering impacts hardware impairments and beam squint. this manuscript, we derive a general signal model that explicitly incorporates impairments, practical pulse shaping functions, squint, overcoming limitations mmWave MIMO models commonly previous work. Then, propose dictionary learning (DL) algorithm obtain embedding by effect squint introducing it into process. We also design novel CS under where structures at different subcarriers exploited enable parameter with low complexity high accuracy. Numerical results demonstrate effectiveness proposed DL strategy when applied realistic channels.