On non-Randomized Hard Decision Fusion Under Neyman-Pearson Criterion Using LRT

Mohammad, Fayazur Rahaman and Khan, Mohammed Zafar Ali (2018) On non-Randomized Hard Decision Fusion Under Neyman-Pearson Criterion Using LRT. In: IEEE 88th Vehicular Technology Conference (VTC-Fall), 27-30 Aug, 2018, Chicago, IL, USA, USA.

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Abstract

The non-randomized optimal hard decision fusion under Neyman-Pearson criterion is known to be an NP-hard classical 0–1 Knapsack problem with exponential complexity. In this paper, we show analytically that though the low-complexity non-randomized single-threshold likelihood ratio based test (non-rand-st LRT) is sub-optimal, its performance approaches the upper-bound obtained by randomized LRT (rand LRT) with the increase in the number of participating sensors (N). This alleviates the need for employing the exponentially complex non-randomized optimal solution for large N. Receiver operating characteristics are plotted to verify the performance of the non-rand-st LRT with reference to the upper-bound obtained by rand LRT for different scenarios.

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IITH Creators:	IITH Creators ORCiD Khan, Mohammed Zafar Ali UNSPECIFIED
Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Neyman-Pearson, decision fusion, cognitive radio, non-randomized tests, likelihood ratio, randomized, IoT, sensors
Subjects:	Electrical Engineering
Divisions:	Department of Electrical Engineering
Depositing User:	Team Library
Date Deposited:	22 Apr 2019 09:14
Last Modified:	22 Apr 2019 09:14
URI:	http://raiithold.iith.ac.in/id/eprint/4986
Publisher URL:	http://doi.org/10.1109/VTCFall.2018.8691021
Related URLs:	Google Scholar

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