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Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

Citation Author(s):
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri
Submitted by:
Mohamed Suliman
Last updated:
2 March 2017 - 1:17pm
Document Type:
Poster
Document Year:
2016
Event:
Presenters Name:
Mohamed Suliman
Paper Code:
4276

Abstract 

Abstract: 

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.

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