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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