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To AND or To OR: On Energy-Efficient Distributed Spectrum Sensing With Combined Censoring and Sleeping
University of Luxembourg.
Delft University of Technology.
University of Luxembourg.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. University of Luxembourg. (Signal Processing)ORCID iD: 0000-0003-2298-6774
2015 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 14, no 8, p. 4508-4521Article in journal (Refereed) Published
Abstract [en]

Distributed spectrum sensing improves the detection reliability of a cognitive radio network but generally comes at the price of a large power consumption. Since cognitive radios are generally low-power sensors with limited batteries, a combined censoring and sleeping scheme is considered as an energy-efficient algorithm for distributed spectrum sensing. Each sensor switches off its sensing module with a specific sleeping rate. When the sensor is on, a censoring policy is employed to send the sensing result to the fusion center. The result is only transmitted, if it is deemed to be informative. Hence, the energy consumption of each sensor, including the sensing and transmission energies, is reduced. The underlying sensing parameters are derived by minimizing the maximum average energy consumption per sensor subject to a lower-bound on the global probability of detection and an upper-bound on the global probability of false alarm. We analyze the problem for the OR and the AND rule and provide a performance analysis for a case study based on the IEEE 802.15.4/ZigBee standard. It is shown that the combined censoring and sleeping scheme achieves a significant energy saving compared to the case where no censoring or sleeping is taken into account.

Place, publisher, year, edition, pages
IEEE, 2015. Vol. 14, no 8, p. 4508-4521
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-259155DOI: 10.1109/TWC.2015.2422304Scopus ID: 2-s2.0-84939518761OAI: oai:DiVA.org:kth-259155DiVA, id: diva2:1350602
Note

QC 20191025

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-10-25Bibliographically approved

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