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Title: Estimation of primary traffic statistics based on spectrum sensing
Author: Al-Tahmeesschi, Ahmed Abdulkareem Jaafar
ISNI:       0000 0004 7659 0211
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2018
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Cognitive Radio (CR) systems can benefit from the knowledge of the activity statistics of primary channels, which can use this information to intelligently adapt their spectrum use to the operating environment and work more efficiently and reduce interference on primary users. Particularly relevant statistics are the minimum, mean and variance of the on/off period durations, the channel duty cycle and the governing distribution. The main aim of this thesis is to improve the estimation of the primary user statistics under different environments. At the beginning of operation, the CR does not have any information about the primary traffic statistics. Spectrum sensing is one of the key methods to obtain this knowledge. Unfortunately, the estimation of primary traffic statistics based on spectrum sensing suffers from some flaws, which are investigated in detail in this thesis. In general, two main working environments for the CRs can be identified based on the primary signal power, namely low and high signal-to-noise ratio (SNR) at the secondary users. For the high SNR scenario, an analytical model to link the sensing period with the observed spectrum occupancy and quantify its impact is proposed. Simulation results show that the proposed model captures with reasonable accuracy the spectrum occupancy observed at the CR. Moreover, the effect of the sample size (number of on/off periods) on the estimated accuracy is studied as well. Closed form expressions to estimate the statistics of the primary channel to a certain desired level of accuracy are derived to link such sample size with the accuracy of the observed primary activity statistics. The accuracy of the obtained analytical results is validated and corroborated with both simulation and experimental results, showing a perfect agreement. For the low SNR scenario, both local and cooperative estimation are considered based on the number of SUs performing the estimation. For the single estimation scenario, 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 an accurate estimation for the primary user statistics. For the cooperative estimation scenario, a new reporting mechanism is proposed in order to increase the spectrum and energy efficiency of the cooperative network and improve resilience under Byzantine attacks. The proposed method is compared in terms of efficiency with methods proposed in the literature and the default periodic reporting method. Simulation results show that the proposed scheme not only reduces significantly the signalling overhead, but with a minor modification it can estimate the primary user distribution under Byzantine attacks with high accuracy. In summary, this thesis contributes a holistic set of mathematical models and novel methods for an accurate estimation of the primary traffic statistics in CR networks based solely on spectrum sensing.
Supervisor: Lopez-Benitez, Miguel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral