Wasserstein Distributionally Robust Adaptive Beamforming
Distributionally robust optimization (DRO)-based robust adaptive beamforming (RAB) enables enhanced robustness against model uncertainties, such as steering vector mismatches and interference-plus-noise covariance matrix estimation errors. Existing DRO-based RAB methods primarily rely on uncertainty sets characterized by the first- and second-order moments. In this work, we propose a novel Wasserstein DRO-based beamformer, using the worst-case signal-to-interference-plus-noise ratio maximization formulation. The proposed method leverages the Wasserstein metric to define uncertainty sets, offering a data-driven characterization of uncertainty. We show that the choice of the Wasserstein cost function plays a crucial role in shaping the resulting formulation, with norm-based and Mahalanobis-like quadratic costs recovering classical norm-constrained and ellipsoidal robust beamforming models, respectively. This insight highlights the Wasserstein DRO framework as a unifying approach, bridging deterministic and distributionally robust beamforming methodologies.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Distributionally Robust Adaptive Beamforming
As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest (SoI) while mitigating interference plus noise (IPN). When uncertainties exist in the signal model…
subspace methodsConfidence Regions in Wasserstein Distributionally Robust Estimation
Wasserstein distributionally robust optimization estimators are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a…
A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization
We present a general duality result for Wasserstein distributionally robust optimization that holds for any Kantorovich transport cost, measurable loss function, and nominal probability distribution. Assuming an intercha…
Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning
We propose an adjusted Wasserstein distributionally robust estimator -- based on a nonlinear transformation of the Wasserstein distributionally robust (WDRO) estimator in statistical learning. The classic WDRO estimator …
regressionExact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Wasserstein distributionally robust estimators have emerged as powerful models for prediction and decision-making under uncertainty. These estimators provide attractive generalization guarantees: the robust objective obt…
Decision MakingDecision Making Under Uncertainty