Electrical Computer Engineering

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Start date: 2020Subject: search.filters.subject.Massive MIMO

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    Comparative Performance Analysis of Channel Estimation Techniques for Massive MIMO System
    (Hawassa University, 2021-12-17) Ashenafi Gebre
    The need for an internet connection is growing universally, so people need much higher data rate connection to meet their need but every physical resource in communication like frequency band, transmit signal strength are finite. Within the given limited resource, higher data speed is accomplished by a new technology called massive Multiple Input Multiple Output (massive MIMO) system. Massive MIMO fulfills the high data rate requirement through antenna diversity gain. It is one of 5G wireless network technology with array antennas at both transmitter and receiver sides to providing high spectral and energy efficiencies. In massive MIMO, the signal obtained by the receiver is in different phase and amplitude from the transmission signal. Therefore, the system quality is highly depending on the accuracy of the channel estimation. Channel estimation plays a significant role in the performances of massive MIMO system because the dimension of the channels matrix is large, phase changes and noises are added when the signals pass through channel, these reduces the efficiencies of the whole system. To solve these problems, this Thesis focuses on the comparative analysis of pilot-based channel estimation schemes for massive MIMO system, which includes: Minimum Mean square Error (MMSE), Element Wise Minimum Mean Square Error (EW-MMSE), Maximum Likelihood (ML) and Least Square (LS) estimator with respect to Normalized Mean square Error (NMSE), Signal to Noise Ratio (SNR), Spectral Efficiency (SE), number of BS antennas, and number of computational complexities. Based on the simulation results, MMSE channel estimator has the best performance, followed by EW-MMSE, ML and LS channel estimators in terms of NMSE and SNR. And depending on the number of computational complexities of each coherent block as a function of BS antennas M with constant UE, the complexity of LS is less than that of MMSE, and it has almost the same complexity as ML and EW-MMSE. And also, according to SE with respect to BS antenna M, the MMSE provides the highest SE using the highest complexity, EW-MMSE and ML achieves a good balance between SE and complexity, and LS has the lowest complexity, but also provides the lowest SE. Finally, the result confirms that MMSE channel estimation technique has the best performance compared to EW-MMSE, ML and LS channel estimators with the cost of high complexity
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    PERFORMANCE ANALYSIS AND COMPARISON OF LINEAR PRECODING TECHNIQUES IN MASSIVE MIMO UNDER DIFFERENT FADING CHANNEL
    (Hawassa University, 2020-10-16) KETEMA TULLU
    Wireless communication and its featuring technology is continuously moving forward because of high demand in wireless communication services. Precoding techniques are very important in future telecommunication technology because in order to increase data rate the simplest method is increasing bandwidth. This method is difficult, because of scarcity in radio-frequency spectrum therefore, research over the last ten years has been focused towards improving spectral efficiency, so that higher data rates can be achieved with in a given band width for all above mentioned scenarios M-MIMO gives practical solution. Massive MIMO technology is now attracting attention of both academic and industry. This motivates us to work on this topic. Most of the studies considered the uplink performance. Here, we study massive MU-MIMO for down-link system with linear precoding. We deliberate the system performance when the number of antennas and the number of users are large. Our area of interest in this work focus on performance analysis and comparison of transmitter precoding so, it is very much essential to know all the aspects of precoding techniques. The main objective of this thesis is to analyze and compare the performance of linear precoding schemes in massive MIMO under Rayleigh fading channel and Rician fading channel. The performance Analysis and comparison issues include number of base station antenna, number of users, signal to noise ratio and Spectral efficiency under both complete and incomplete channel state information at the transmitter (CSIT) using MATLAB software. Finally from this thesis work it was observed that as the number of users increases the mathematical complexity of SINR and spectral efficiency increases for all MF, ZF and RZF linear precoding schemes. Generally, in thesis work it is observed that ZF gives better performances at all transmission power and in an increasing number of BS antenna over RZF and MF in both perfect and imperfect channel state information under Rayleigh and Rician fading channel.
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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