Robust Transmit Beamforming for Parameter Estimation Using Distributed Sensors
In this letter, we study the problem of estimating a complex parameter in a wireless sensor network under individual power constraints and bounded channel uncertainty, where the channel state information is assumed to be imperfect at both the sensors and the fusion center. Transmit beamformers and a...
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| Veröffentlicht in: | IEEE communications letters Jg. 20; H. 7; S. 1329 - 1332 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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New York
IEEE
01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1089-7798, 1558-2558 |
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| Abstract | In this letter, we study the problem of estimating a complex parameter in a wireless sensor network under individual power constraints and bounded channel uncertainty, where the channel state information is assumed to be imperfect at both the sensors and the fusion center. Transmit beamformers and a linear estimator that minimize the maximum mean square error are found. Although this problem is nonconvex, it can be relaxed to become a semidefinite programming problem by re-parameterization and semidefinite relaxation. Surprisingly, we find that relaxation is tight and a global optimal solution can be found. Finally, numerical simulations are performed to evaluate the performance of the robust estimator. |
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| AbstractList | In this letter, we study the problem of estimating a complex parameter in a wireless sensor network under individual power constraints and bounded channel uncertainty, where the channel state information is assumed to be imperfect at both the sensors and the fusion center. Transmit beamformers and a linear estimator that minimize the maximum mean square error are found. Although this problem is nonconvex, it can be relaxed to become a semidefinite programming problem by re-parameterization and semidefinite relaxation. Surprisingly, we find that relaxation is tight and a global optimal solution can be found. Finally, numerical simulations are performed to evaluate the performance of the robust estimator. |
| Author | Jiang Zhu Blum, Rick S. Xiaokang Lin Yuantao Gu |
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| Cites_doi | 10.1109/TSP.2009.2038961 10.1515/9781400831050 10.1017/CBO9780511810817 10.1109/TSP.2010.2062178 10.1109/TWC.2010.091510.091836 10.1137/S003614450444614X 10.1109/TSP.2004.828931 10.1109/TSP.2014.2373332 10.1109/LSP.2011.2123092 10.1109/TSP.2010.2082537 10.1109/TSP.2009.2021451 10.1109/TSP.2009.2021456 |
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| SubjectTerms | Beamforming Channels collaborative beamforming Estimation Estimators Mathematical models Mean square values Optimization Optimized production technology Remote sensors Robust estimation Robustness SDP SDR Sensor fusion Sensors Uncertainty |
| Title | Robust Transmit Beamforming for Parameter Estimation Using Distributed Sensors |
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