Abdelhak, M. Zoubir (Prof.)Dereje, Hailemariam (PhD) Co-Supervisor:Amare, Kassaw2021-06-052023-11-282021-06-052023-11-282021-03http://etd.aau.edu.et/handle/12345678/26677Next generation networks are expected to support large volume of data tra c generated from emerging applications such as ultra high speed video streaming, machine-tomachine (M2M) communication and the Internet of things (IoT). To handle this large volume of data tra c, these networks should employ technologies that utilize broad spectrum, o er higher cell density and high spectral e ciency. The spectral e ciency can be improved by increasing the transmitter power; introducing additional processing (such as deploying multiple antenna systems and advanced modulation techniques) in transceiver pairs that help to harvest energy; minimize multiuser-interference; and implementing innovative wireless planning and operation strategies that save energy. By deploying very large numbers of antennas at a base station (BS), which is called massive multiple input multiple output (MIMO), we can signi cantly improve the spectral e ciency of mobile networks. Besides, massive MIMO simpli es transmission processing, improves energy e ciency and reduces the required transmission power of the users. Due to those performance gains, massive MIMO has become an enabler for the deployment of 5G and beyond networks. In this PhD research, we study and analyze channel modeling, resource allocation and optimization techniques in massive MIMO systems. For this, rst we analyze recent works on signal processing, channel modeling, channel estimation, resource allocation and optimization techniques in massive MIMO systems. In this regard, fundamentals of massive MIMO systems including channel capacity, spectral e ciency and energy e ciency have been studied. Closedform lower bound expressions are derived for the spectral e ciency and energy e ciency. Then, simulation results are provided to validate the theoretical analysis. Besides, performance analysis is done for linear detection and precoding techniques and then computationally e cient inverse approximation techniques are proposed for linear detection and precoding in massive MIMO systems. Speci cally, Truncated Neumann series-based matrix inversion approximation techniques are formulated and probability of convergence, error of approximation and computationally complexity are analyzed. Then, we analyze achievable spectral e ciency of massive MIMO systems in realistic propagation environment under perfect and imperfect channel state information (CSI) scenarios. In particular, the e ects of major large scale and small fading parameters including pathloss, shadowing, multipath fading, spatial channel correlation and impact of channel estimation have been investigated. Spectral e ciency analysis is done for uplink massive MIMO system under Rician fading channel model. Besides, by applying non-central to central Wishart approximation, closedform lower bound achievable rate expressions are formulated for massive MIMO systems in Rican fading channel model. Then, energy e cient power control and resource allocation algorithms have been proposed. For this, rst by using large system analysis, analytical closedform lower bound expressions are derived for the achievable sum rate and appropriate power consumption model is formulated for the proposed massive MIMO systems. Then, by utilizing tools from fractional programming theory and sequential convex programming, energy e cient power control and resource allocation algorithms have been formulated. Further, the impacts of system and propagation parameters on energy e ciency have been evaluated. Particularly, the impacts of maximums transmitter power and minimum rate constraints of the users on global energy e ciency have been evaluated. The results show that the global energy e ciency increases with the maximum transmitter power constraint and decreases with the minimum data rate constraint. Finally, we analyze the performance of multicell massive MIMO systems in spatially correlated channel model. First, we study and evaluate fundamentals of multicell massive MIMO systems. In this regard channel modeling, power allocation and spatial resource allocation in multicell massive MIMO systems are considered. Besides, we evaluate the impacts of spatial correlation and pilot contamination. Important trade-o s and considerations on design and optimization of multicell massive MIMO systems has been studied. The impacts of system and propagation parameters are evaluated theoretically and via numerical simulation. The results show that spatial channel correlation has a major impact on channel hardening, favorable propagation, channel estimation quality and spectral e ciency of the system.en-US5Gmassive MIMOspectral effciencyenergy effciencyoptimizationresource allocationThesis