Electrical Computer Engineering

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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
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    PERFORMANCE ANALYSIS AND COMPARISON OF ENERGY EFFICIENT MASSIVE MIMO ANTENNA SELECTION ALGORITHMS
    (Hawassa University, 2020-10-18) ABAYINEH TECHANE HORDOFA
    Massive multi-input multi-output system plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station of a network to improve cell capacity for network communication systems and this technology employs a lot amount of antennas at the base station (BS) and can reach high data rates under favorable propagation conditions and using simple linear processing. However massive MIMO downlink systems have some drawbacks, such as the high bulk antenna which leads power consumption device at the base stations, so that the power consumptions of the radio frequency chains can be huge, which poses great challenges. All radio frequency (RF) chains required in BS equipped with each number of transmit antennas this implies the hardware energy consumption may not significantly increase. A way to deal with this issue is to utilize antenna selection algorithms and through assuming equal power allocation among the users at the Base Stations. Antenna selection algorithm scheme is one method to achieve sum-rate and assess energy efficiency in massive MIMO systems and reducing the number of RF chains transmitter out of M transmitter antenna. The main aim of this thesis work to analyze and compare energy efficient Massive MIMO antenna selection algorithms. The selected energy efficient massive MIMO antenna selection algorithms are random antenna selection (RASA), norm based antenna selection (NBASA) and greedy antenna selection algorithm (GASA) to select the sub-optimal set of the number of antennas that produce attain sum-rate from the available M antennas at BS of the massive downlink MIMO system at perfect CSI. We compare the performance of massive MIMO antenna selection based on achieved sum-rate, transmitted number of selected antenna, M transmit antennas, users, SNR and energy efficiency and simulate those antenna selection schemes using matlab software. As we obtain from simulation result the greedy antenna selection algorithm leads to best achieved sum-rate and energy efficiency than NBASA and RASA under total power constraint in massive MIMO systems