Sparse Array Beamformer Design via ADMM

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)3357-3372
Journal / PublicationIEEE Transactions on Signal Processing
Volume71
Online published14 Sept 2023
Publication statusPublished - 2023

Abstract

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. © 2023 IEEE.

Research Area(s)

  • Adaptive beamforming, ADMM, output SINR, semidefinite relaxation, sparse array design, successive convex approximation

Citation Format(s)

Sparse Array Beamformer Design via ADMM. / Huang, Huiping; So, Hing Cheung; Zoubir, Abdelhak M.
In: IEEE Transactions on Signal Processing, Vol. 71, 2023, p. 3357-3372.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review