Reconstruction Algorithm for Primary Channel Statistics Estimation Under Imperfect Spectrum Sensing

Toma, Ogeen H ORCID: 0000-0002-3969-0470, Lopez-Benitez, Miguel ORCID: 0000-0003-0526-6687, Patel, Dhaval K and Umebayashi, Kenta (2020) Reconstruction Algorithm for Primary Channel Statistics Estimation Under Imperfect Spectrum Sensing. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020-5-25 - 2020-5-28.

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Official URL: http://dx.doi.org/10.1109/wcnc45663.2020.9120788

Abstract

Statistical information of primary channels has received considerable research interest in the recent years. This is due to the important role that these statistics play in improving the performance of Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems. Although a DSA/CR system has no initial knowledge about the statistical information of the primary channels, these statistics can be estimated from the observations of spectrum sensing. However, spectrum sensing is not perfect in the real world and sensing errors are likely to occur during DSA/CR operation, which in turn leads to incorrect estimation of primary channel statistics as well. As a result, several attempts have arisen to reconstruct the estimated periods of the primary channel occupancy patterns which are affected by the sensing errors, in order to provide more accurate estimation for the statistical information. However, all the reconstruction methods available in the literature assume the perfect knowledge of the primary users' minimum occupancy time. In this context, this work proposes the first reconstruction method that does not require any prior knowledge about the primary channel activity and inactivity patterns while achieving almost the same performance achieved by the latest reconstruction methods available in the literature, making it significantly attractive and feasible in practical implementation scenarios.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Cognitive radio, dynamic spectrum access, spectrum sensing, primary channel statistics, reconstruction algorithms
Depositing User:	Symplectic Admin
Date Deposited:	29 Jan 2020 15:36
Last Modified:	19 Jan 2023 00:06
DOI:	10.1109/wcnc45663.2020.9120788
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3072442