论文标题
通过不确定性集的最大化最大化，可鲁棒的自适应波束形成
Robust Adaptive Beamforming via Worst-Case SINR Maximization with Nonconvex Uncertainty Sets
论文作者
论文摘要
本文考虑了基于最差的信号与互换 - 加上噪声比（SINR）最大化的鲁棒自适应波束形成（RAB）问题的表述，并为转向向量提供了非凸的不确定性集。不确定性集由相似性约束和（非凸）双面球约束组成。最坏的case SINR最大化问题使用半限制编程的强双重性转变为二次矩阵不等式（QMI）问题。然后提出了QMI问题的线性矩阵不等式（LMI）松弛，并具有额外的有效线性约束。建立了加强LMI松弛问题的必要条件，以建立排名一的解决方案。当收紧的LMI松弛问题仍然具有高级解决方案时，LMI松弛问题进一步限制为双线性基质不等式（BLMI）问题。然后，我们提出了一种迭代算法来解决BLMI问题，该问题通过求解BLMI公式，为原始RAB问题找到了最佳/次优的解决方案。为了验证我们的结果，提出了仿真示例，以证明提出的稳健光束形式的改进阵列输出sinr。
This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer.
