LAUSR

Antenna Array imperfection calibration is an important concern in direction‐of‐arrival (DOA) estimation in 5 G millimeter Wave (mmWave) massive Multiple Input Multiple Output (mMIMO) systems. As the number of elements in mMIMO systems increases, array imperfections tend to increase, degrading the DOA estimation performance. Existing calibration techniques use the local optimum solution as the gain/phase, and the location error is high. The present work proposes an inter‐disciplinary learning teaching‐learning‐based optimization (IDL‐TLBO) to estimate the DOA. This algorithm exploits the joint sparse properties of the DOA vector and array perturbation matrix. Benefitting from inter‐disciplinary learning and sparse properties, the global search capability of the proposed method enhances the accuracy of DOA estimation. The efficacy of the proposed IDL‐TLBO was validated using various simulation scenarios. Simulation results reveal that the proposed method achieves a better performance‐complexity trade‐off than conventional methods for DOA estimation.