Robust Adaptive Beamforming Based on Deep Unfolded ADMM Network
The gain-phase errors between different array channels lead to steering vector mismatch,which seriously degrades the performance of adaptive beamforming.The current robust adaptive beamforming(RAB)methods improve beamforming performance by introducing worst-case steering vector mismatch error constraints or jointly estimating gain-phase errors and beamformer weight vector.However,these methods consume high computational overhead and have poor performance under limited snapshot scenes.Owing to this,this article proposes an alternating direction multiplier method(ADMM)-based RAB network under the deep unfol-ding framework to quickly achieve joint estimation of gain-phase errors and interference covariance matrix.The sparse representa-tion model of interference signals is first established in the presence of gain-phase errors.Then,according to the ADMM-based joint estimation method of gain-phase errors and sparse coefficients of interferences,a deep unfolding ADMM(DU-ADMM)net-work is proposed.Its input is the received interference signals and its output is gain-phase errors and interference sparse coeffi-cients.Finally,the interference plus noise covariance matrix is recovered based on the network output and then used to generate a robust adaptive beamformer.Simulation results show that the DU-ADMM network can achieve RAB in a single snapshot scene.Mo-reover,it can estimate the gain-phase errors more accurately with fewer network layers,leading to reduced computational cost,and yield higher output signal-to-interference-plus-noise ratio.
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