| Paper: | SS-3.2 | ||
| Session: | Convex Optimization Methods for Signal Processing and Communications | ||
| Time: | Tuesday, May 16, 16:50 - 17:10 | ||
| Presentation: | Special Session Lecture | ||
| Topic: | Special Sessions: Convex optimization methods for signal processing and communications | ||
| Title: | Robust Minimum Variance Adaptive Beamformers and Multiuser MIMO Receivers: From the Worst-Case to Probabilistically Constrained Designs | ||
| Authors: | Sergiy Vorobyov, Yue Rong, Alex B. Gershman, Darmstadt University of Technology, Germany | ||
| Abstract: | Two related problems of the design of robust adaptive beamformers and multiuser multiple-input multiple-output (MIMO) receivers are considered. A popular recent solution to these problems is based on the worst-case performance optimization. Unfortunately, in practical applications the actual worst case occurs with a very low probability and, as a result, the worst-case based designs may be overly conservative. As a less conservative alternative to the worst-case designs, the so-called probabilistically constrained designs are introduced. The latter approach guarantees that the distortionless response constraint is satisfied for a mismatched array response with a certain selected probability. Improved flexibility and performance of the robust probabilistically constrained designs with respect to the worst-case designs are illustrated via simulations. | ||
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