A Penalized Inequality-Constrained Approach for Robust Beamforming with DoF Limitation

TL;DR

This paper introduces a penalized inequality-constrained convex optimization approach for robust beamforming with limited degrees of freedom, effectively balancing interference mitigation, noise reduction, and target protection.

Contribution

It proposes a novel P-ICMV beamformer formulation using SOCP and ADMM, enhancing robustness and efficiency in DoF-limited scenarios.

Findings

Effective interference suppression demonstrated in simulations

Robustness against steering vector mismatch improved

Low complexity iterative algorithm outperforms traditional methods

Abstract

A well-known challenge in beamforming is how to optimally utilize the degrees of freedom (DoF) of the array to design a robust beamformer, especially when the array DoF is limited. In this paper, we leverage the tool of constrained convex optimization and propose a penalized inequality-constrained minimum variance (P-ICMV) beamformer to address this challenge. Specifically, a well-targeted objective function and inequality constraints are proposed to achieve the design goals. By penalizing the maximum gain of the beamformer at any interfering directions, the total interference power can be efficiently mitigated with limited DoF. Multiple robust constraints on the target protection and interference suppression can be introduced to increase the robustness of the beamformer against steering vector mismatch. By integrating the noise reduction, interference suppression, and target protection, the proposed formulation can efficiently obtain a robust beamformer design while optimally trading off various design goals. To numerically solve this problem, we formulate the P-ICMV beamformer design as a convex second-order cone program (SOCP) and propose a low complexity iterative algorithm based on the alternating direction method of multipliers (ADMM). Three applications are simulated to demonstrate the effectiveness of the proposed beamformer.