Power scheduling for distributed multiple-hypothesis detection by task-specific information
- Authors
- Kim, H.-S.; Yang, S.-I.
- Issue Date
- 2015
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- classification; distributed classification; distributed detection; multiple hypotheses; mutual information; optimal power allocation; Rayleigh fading channel; slow fading; task-specific information
- Citation
- 2015 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2015
- Indexed
- SCOPUS
- Journal Title
- 2015 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2015
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/19275
- DOI
- 10.1109/ICSPCC.2015.7338862
- ISSN
- 0000-0000
- Abstract
- We introduce a new information theoretic power allocation scheme applicable to distributed multiple-hypothesis detection systems communicating over slow fading channels. In earlier work, it was demonstrated that performance could be improved by adjusting transmit power to maximize the J-divergence measure of a binary detection system and the J-divergence method is extended for a distributed multiple-hypothesis detection system by defining pairwise sums of the J-divergences. However, the pairwise sum measure does not provide a tight bound. Basically, the more hypotheses we adopt, the less efficient the optimization is. Thus, we derive a more efficient classification-oriented information measure for power optimization of distributed multiple-hypothesis system by introducing a virtual decider variable. The virtual decider variable is directly related with classification task. Various numerical results are also shown to compare the performances. ? 2015 IEEE.
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