Compressive Spatial Channel Estimation Under IQ Imbalance

In-phase and quadrature-phase (IQ) imbalance in high-frequency systems distorts received measurements, causing standard channel estimation algorithms that ignore this mismatch to fail. In this paper, we develop an augmented compressed sensing (CS) model to jointly estimate the sparse channel and the IQ imbalance (IQI) parameter in the form of an augmented CS vector. This vector exhibits group sparsity, which is exploited using our tailored paired-support orthogonal matching pursuit (PSOMP) algorithm. Finally, the estimate from PSOMP is decomposed to determine the channel and the IQI parameter. Numerical results show that our method achieves better support recovery and a lower error in the estimated channel than the baselines.