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Author: search.filters.author.TSEGA TEKLEWOLD TEKLEMARIAMSubject: search.filters.subject.Energy efficiency

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    ENERGY EFFICIENCY ANALYSIS OF COMPRESSIVE SENSING BASED COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO
    (Hawassa University, 2020-10-22) TSEGA TEKLEWOLD TEKLEMARIAM
    A range is a scant and valuable asset and a matter of worry with the quickly developing wireless communications. Wireless communication industries are increasing at a very fast pace as the wireless technologies are attracting the interest of many users, so the demand increases and researchers are looking for alternative adaptive measures. The essential usefulness to empower dynamic range access for CR is wideband spectrum sensing to discover more temporarily available frequency bands to fulfill the growing needs of wireless services. Also, the need to occasionally detect and an expansion in the quantity of channels to be detected further builds the energy interest. Thus, one of the principal challenges that limit the implementation of cognitive radio networks especially in the battery-powered terminal is due to its high energy consumption. Consequently, energy-proficient CSS in a CR network utilizing the CS based maximum minimum subband ED is the focal point of this thesis. The number of participating CRs in the cooperative spectrum sensing, sensing duration, data transmission duration, and fusion threshold play vital roles in designing an energy-efficient CSS system. In other words, increasing the number of participating CRs in the system leads to an increase in both consumed energy during CSS process and delay time; moreover, longer sensing time duration increases detection precision, but on the other hand, decreases spectrum efficiency and increases the consumed energy during sensing phases (i.e., sensing overhead).In the essence of above-mentioned facts, tackling a trade-off between performance improvement and overhead is our main focus research point in this thesis. Evaluation and analysis of performance are done by using MATLAB software. The simulation result shows that the Compressive sensing-based Max-Min subband ED has better performance than traditional Max-Min subband ED based on Shannon-Nyquist sampling theorem. Also, it shows that strategies remarkably increase the energy efficiency of the cooperative system; furthermore, it is shown optimality of Majority rule over other two hard decision fusion rules. Finally, optimization of sensing time, number of sensing users and fusion threshold for a cognitive radio is considered. Finally, the energy efficiency is enhanced by 74.6% when compared with the conventional energy detection-based EE