Residual MLP-based over-the-air calibration for phased arrays: A low-complexity and efficient method

This paper proposes a residual multi-layer perceptron (MLP)-based approach for phase calibration in phased array antennas, aiming to improve computational efficiency while maintaining high calibration accuracy. Conventional calibration methods, such as the Greedy algorithm, achieve accurate results by exhaustively searching all possible phase states of phase shifters; however, the conventional method suffers from severe computational inefficiency, particularly for large-scale arrays or systems that require frequent calibration. To address this limitation, the proposed residual MLP is trained on the optimal phase vectors obtained from the conventional method, which eliminates the need for exhaustive phase searches. The residual MLP-based calibration framework achieves nearly the same accuracy as the conventional method while reducing calibration time by more than an order of magnitude. The proposed approach is therefore highly suitable for practical phased array applications that demand fast and frequent calibration.