Abstract
In the context of distributed estimation, we study the problem of sensor collaboration with individual power constraints, where sensor collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. In order to find the optimal collaboration strategy consisting of collaboration topology and power allocation scheme, we propose a non-convex formulation in which the estimation distortion is minimized subject to individual power constraints. By exploiting the problem structure, locally optimal collaboration strategies are found via bilinear relaxations and a convex-concave procedure. Numerical examples are provided to show the effectiveness of our approach.
Original language | English (US) |
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Title of host publication | Conference Record - Asilomar Conference on Signals, Systems and Computers |
Publisher | IEEE Computer Society |
Pages | 571-575 |
Number of pages | 5 |
Volume | 2016-February |
ISBN (Print) | 9781467385763 |
DOIs | |
State | Published - Feb 26 2016 |
Event | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015 |
Other
Other | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
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Country | United States |
City | Pacific Grove |
Period | 11/8/15 → 11/11/15 |
Keywords
- bilinear relaxation
- convex-concave procedure
- Distributed estimation
- networks
- sensor collaboration
- ℓ1 norm
ASJC Scopus subject areas
- Computer Networks and Communications
- Signal Processing