Abstract
A distributed belief propagation protocol is developed to carry inference and decoding tasks using wireless sensor networks with high-dimensional, correlated observations. Statistical dependencies are modeled using factor graphs. The overall a-posteriori probability is factored so that its factor graph representation can be mapped to the actual communication network. Sum-productmessage passing updates over the graphical model can thus be mapped to messages among sensors. As an application scenario, distributed spectrum sensing is considered. Simulated tests show that exploiting the correlation present among sensor observations can considerably improve sensing performance.
Original language | English (US) |
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Title of host publication | 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings |
Pages | 2841-2844 |
Number of pages | 4 |
DOIs | |
State | Published - Oct 23 2012 |
Event | 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan Duration: Mar 25 2012 → Mar 30 2012 |
Other
Other | 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 |
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Country/Territory | Japan |
City | Kyoto |
Period | 3/25/12 → 3/30/12 |