论文标题
稀疏的阵列边界设计通过ADMM
Sparse Array Beamformer Design via ADMM
论文作者
论文摘要
在本文中，我们设计了一种稀疏的阵列设计算法，用于自适应波束形成。我们的策略是基于找到稀疏的光束型重量，以最大程度地提高输出信号与差异比率（SINR）。提出的方法利用乘数的交替方向方法（ADMM），并在每个ADMM迭代中接受封闭形式的解决方案。通过显示增强拉格朗日函数的单调性和界限来分析算法收敛属性。此外，我们证明了所提出的算法会收敛到Karush-Kuhn-Tucker固定点的集合。数值结果表现出了出色的性能，与详尽的搜索方法相当，比最先进的求解器的搜索方法略好，包括半芬矿放松（SDR），其变体（SDR-V）和连续的凸近近似值（SCA）方法，并且显着的其他几个Sparse Sparse Drangay Drange Draction策略策略sinr oftuct sinr s in Offuct sinr senr senr senr contray and oft sinr。此外，就计算复杂性而言，提出的ADMM算法优于SDR，SDR-V和SCA方法。
In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-to-interference-plus-noise ratio (SINR). The proposed method utilizes the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. The algorithm convergence properties are analyzed by showing the monotonicity and boundedness of the augmented Lagrangian function. In addition, we prove that the proposed algorithm converges to the set of Karush-Kuhn-Tucker stationary points. Numerical results exhibit its excellent performance, which is comparable to that of the exhaustive search approach, slightly better than those of the state-of-the-art solvers, including the semidefinite relaxation (SDR), its variant (SDR-V), and the successive convex approximation (SCA) approaches, and significantly outperforms several other sparse array design strategies, in terms of output SINR. Moreover, the proposed ADMM algorithm outperforms the SDR, SDR-V, and SCA methods, in terms of computational complexity.
