Reduced Complexity Optimal Hard Decision Fusion under Neyman-Pearson Criterion

D, Nikhil and Khan, Mohammed Zafar Ali (2018) Reduced Complexity Optimal Hard Decision Fusion under Neyman-Pearson Criterion. Masters thesis, Indian Institute of Technology Hyderabad.

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Abstract

Distributed detection is an important part of many of the applications like wireless sensor networks, cooperative spectrum sensing in the cognitive radio network. Traditionally optimal non-randomized hard decision fusion rule under Neyman Pearson(NP) criterion is exponential in complexity. But recently [4] this was solved using dynamic programming. As mentioned in [4] that decision fusion problem exhibits semi-monotonic property in a special case. We use this property in our simulations and eventually apply dynamic programming to solve the problem with further reduced complexity. Further, we study the e�ect of using multiple antennas at FC with reduced complexity rule.

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