TY - JOUR
T1 - Uniformly Most Powerful Distributed Detection and its Application in Cooperative Spectrum Sensing
AU - Chen, Hao
AU - Rogers, Uri
PY - 2011/11/6
Y1 - 2011/11/6
N2 - In this paper, a special class of distributed composite binary hypothesis testing problem with monotonic likelihood ratio is investigated. The sensor observations are assumed to be conditionally independent given a fixed but unknown parameter θ where θ ∈ Θ 1 under the H 1 hypothesis and θ = θ 0 under the H 0 hypothesis. The optimal form of sensor decision rule is established under both the Neyman-Pearson and Bayesian criteria. As an illustrative example, the design of an optimal cognitive radio rule for cooperative spectrum sensing is established.
AB - In this paper, a special class of distributed composite binary hypothesis testing problem with monotonic likelihood ratio is investigated. The sensor observations are assumed to be conditionally independent given a fixed but unknown parameter θ where θ ∈ Θ 1 under the H 1 hypothesis and θ = θ 0 under the H 0 hypothesis. The optimal form of sensor decision rule is established under both the Neyman-Pearson and Bayesian criteria. As an illustrative example, the design of an optimal cognitive radio rule for cooperative spectrum sensing is established.
KW - Gaussian noise
KW - cognitive radio
KW - optimization
KW - random variables
KW - sensors
KW - testing
KW - wireless sensor networks
UR - https://scholarworks.boisestate.edu/electrical_facpubs/194
UR - http://dx.doi.org/10.1109/ACSSC.2011.6190304
U2 - 10.1109/ACSSC.2011.6190304
DO - 10.1109/ACSSC.2011.6190304
M3 - Article
JO - 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)
JF - 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)
ER -