Improving Primary Statistics Prediction Under Imperfect Spectrum Sensing



Al-Tahmeesschi, A ORCID: 0000-0002-5750-5080, Lopez-Benitez, M ORCID: 0000-0003-0526-6687, Lehtomäki, J and Umebayashi, K
(2018) Improving Primary Statistics Prediction Under Imperfect Spectrum Sensing. In: IEEE Wireless Communications and Networking Conference (WCNC 2018), 4th IEEE International Workshop on Smart Spectrum (IWSS 2018), 2018-4-16 - 2018-4-16, Barcelona, Spain.

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Abstract

Dynamic Spectrum Access (DSA) / Cognitive Radio (CR) systems utilize spectrum sensing to monitor spectrum status and decide transmission time in an opportunistic manner. This results in an increase in wireless spectrum efficiency. Spectrum sensing can also be used to monitor the statistics of primary users to gain information on occupation patterns and estimate the statistics of the primary traffic activity, a useful knowledge that can be exploited in many ways. In this research, three novel algorithms are proposed to enhance the estimation of primary user activity statistics under imperfect spectrum sensing given the knowledge of minimum transmission time. Simulation results show that the proposed methods enable accurate estimation for the primary user statistics. Moreover, the proposed methods are compared to previously proposed methods and it is shown they provide a significantly better estimation accuracy.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Cognitive radio, dynamic spectrum access, spectrum sensing, spectrum awareness, primary activity statistics
Depositing User: Symplectic Admin
Date Deposited: 02 Feb 2018 15:58
Last Modified: 04 Jun 2024 07:53
DOI: 10.1109/wcnc.2018.8377259
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3017363