TY - JOUR

T1 - Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

AU - Suliman, Mohamed Abdalla Elhag

AU - Ballal, Tarig

AU - Kammoun, Abla

AU - Al-Naffouri, Tareq Y.

N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by a CRG3 Grant ORS#221 from the Office of Competitive Research, King Abdullah University of Science and Technology.

PY - 2016/10/6

Y1 - 2016/10/6

N2 - In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and, hence, the solution of the estimation problem. Relying on the randomness of the model matrix, a number of deterministic equivalents from random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various estimated signal characteristics. In addition, simulations show that our approach is robust in the presence of model uncertainty.

AB - In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and, hence, the solution of the estimation problem. Relying on the randomness of the model matrix, a number of deterministic equivalents from random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various estimated signal characteristics. In addition, simulations show that our approach is robust in the presence of model uncertainty.

UR - http://hdl.handle.net/10754/627641

UR - http://ieeexplore.ieee.org/document/7585088/

UR - http://www.scopus.com/inward/record.url?scp=84994705280&partnerID=8YFLogxK

U2 - 10.1109/LSP.2016.2615683

DO - 10.1109/LSP.2016.2615683

M3 - Article

VL - 23

SP - 1727

EP - 1731

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

SN - 1070-9908

IS - 12

ER -