Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
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AbstractIn 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.
CitationSuliman M, Ballal T, Kammoun A, Al-Naffouri TY (2016) Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory. IEEE Signal Processing Letters 23: 1727–1731. Available: http://dx.doi.org/10.1109/LSP.2016.2615683.
SponsorsThis work was supported by a CRG3 Grant ORS#221 from the Office of Competitive Research, King Abdullah University of Science and Technology.
JournalIEEE Signal Processing Letters