Convolutional Block Attention Module-Based Neural Network for Enhanced IQ Imbalance Estimation in Low Signal-to-Noise Ratio Environments

In cognitive communication networks, the high frequency and wide bandwidth of millimeter-wave signals exacerbate in-phase/quadrature-phase (IQ) imbalance in the transceiver hardware, leading to mirror signal interference, which increases the false alarm probability of spectrum sensing and results in significant performance degradation in communication systems. However, traditional IQ imbalance estimation algorithms typically estimate IQ amplitude and phase imbalance separately, leading to increased hardware costs. To address this issue, we propose a convolutional block attention module (CBAM)-based algorithm to jointly estimate the IQ amplitude and phase imbalance parameters. By exploiting the correlation between IQ amplitude and phase imbalances, the proposed algorithm can not only further improve the accuracy of IQ amplitude and phase imbalance estimation at low signal-to-noise ratios, but also improve the estimation speed through joint estimation scheme. Simulation results demonstrate that the proposed algorithm has lower processing delay and higher estimation accuracy compared to traditional algorithms, and lower space complexity than other deep learning-based algorithms.