Large-Scale Robust Beamforming via l-Minimization

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

14 Scopus Citations
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Original languageEnglish
Pages (from-to)3824-3837
Journal / PublicationIEEE Transactions on Signal Processing
Issue number14
Online published29 May 2018
Publication statusPublished - 15 Jul 2018


In this paper, the linearly constrained and robust l-norm beamforming techniques are proposed for non-Gaussian signals. Conventional approach for l-minimization needs to solve a linear programming (LP) or second-order cone programming (SOCP). However, this strategy is computationally prohibitive for "big data" because the existing algorithms for LP or SOCP, such as simplex method or interior point method, can only solve small- or medium-scale problems. In this work, the alternating direction method of multipliers (ADMM) is devised for large-scale l-beamforming problems, where the core subproblems can be formulated concisely as a linearly or second-order cone constrained least squares and the proximity operator of the l-norm in each iteration. Remarkably, a linear-time complexity algorithm is devised which efficiently computes the l-norm proximity operator. Simulation results verify the high efficiency of the ADMM and the superiority of the l-norm beamforming techniques over several representative beamformers, indicating that its performance can approach the optimal upper bound.

Research Area(s)

  • alternating direction method of multipliers (ADMM), Array signal processing, Beamforming, Complexity theory, Convex functions, Interference, l∞-minimization, Minimization, proximity operator, Robustness, steering vector mismatch, Uncertainty

Citation Format(s)

Large-Scale Robust Beamforming via l-Minimization. / Jiang, Xue; Chen, Jiayi; So, Hing Cheung et al.
In: IEEE Transactions on Signal Processing, Vol. 66, No. 14, 15.07.2018, p. 3824-3837.

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