Communication Engineering

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http://etd.aau.edu.et/handle/12345678/191

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Browsing Communication Engineering by Subject "5G"

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    Analysis of Energy Efficient Techniques for 5G Ultra Dense Wireless Communication Networks Using Massive MIMO
    (Addis Ababa University, 2021-10) Halefom, Tswaslassie; Yihenew, Wondie (PhD)
    In the modern wireless communication energy consumption becomes critical issue for wireless network operators. With the emergence of 5G wireless communication , the importance of energy efficiency (EE) has been appreciated since it is one of the significant performance analysis metrics of wireless networks. Energy can be saved in the design of wirless network if a proper analysis and design optimization is done. Massive MIMO and cell densfications are the latest encouraging technologies to maximize energy efficiency of 5G wireless communications. This thesis work mainly aims on the analysis of energy efficiency techniques of 5G wireless communication using Massive MIMO technology.The techniques to be analysis are in the precoding , in channel state information and massive MIMOtechnology. The analysis begins from circuit power consumption model using zero forcing precoding schemes with TDD communication protocol. The main design parameters are the number of massive antennas at the base station (M), the number of active user equipment terminals (K) , the system throughput (R) and cell density . Then EE is defined as the number of bits transferred per Joule of energy consumed. MATLAB tool is used to prove the impact of the main design parameters on energy efficiency. The impact of massive number of antenna , user equipments and system throughput on energy efficiency with perfect channel state information and imperfect channel state information is analyze . The simulation result shows that we can design optimal values of (M, K and R) that maximize energy efficiency of the system with perfect channel state information than imperfect channel state at the base station. The final results sows that zerforcing precoding and perfect channel state information at the base station saves more energy as compared to iperfect channel state information.
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    Deep Learning-Powered Equalization with Autoencoders for Improved 5G Communication
    (Addis Ababa University, 2024-05) Adomeas Asfaw; Tsegamlak Terefe (PhD)
    The Fifth Generation (5G) wireless technology has significant advancements in communication speed, capacity and latency, revolutionizing various industries and enabling transformative applications. However, these benefits are challenged by the complexities of the wireless environment, characterized by multipath propagation, fading, and interference. This thesis address the challenge of mitigating errors within 5G communication systems. The multipath propagation and fading present in wireless channels often lead to Inter Symbol Interference (ISI) and other forms of distortion. As a mitigation for this cases an autoencoder-based equalizer tailored for 5G communication systems is proposed and thoroughly evaluated. Leveraging the power of deep learning, the autoencoder architecture is adept at extracting complex features from received signals, thus enabling equalization in the presence of channel impairments. Our focus is on mitigating errors within the context of the International Telecommunication Union (ITU) 2020 channel model and Quadrature Amplitude Modulation (QAM) schemes (16-QAM and 64-QAM). Through simulation the performance of the proposed equalizer is assessed using constellation plots, Symbol Error Rate (SER), Bit Error Rate (BER) and convergence rate. Results indicate that the designed autoencoder achieved an SER of approximately 10−4 and a BER of 10−5 for the 16-QAM and an SER of approximately 10−3 and a BER of 10−4 for the 64-QAM. Our comparison analysis reveals the efficacy and competitiveness of the autoencoder-based equalizer in mitigating the effects of the channel for 5G downlink outdoor to indoor communication system.
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    On the Performances of User Association Enhancements in Dense Wireless Heterogeneous Networks
    (Addis Ababa University, 2023-03) Dinkisa, Aga; Yihenew; Hamalainen, Jyri (Prof.); Yihenew, Wondie (PhD)
    User Association (UA) plays a signi cant role in radio resource management of wireless communication systems. Currently, network densi cation and heterogeneity have already been identi ed as a feasible solution for the exponentially expanding data service demand. Hence, UA methods must meet di erent requirements in dense and ultra dense Heterogeneous Networks (HetNets). The load-imbalance due to transmit power di erence between tiers and interference coordination challenges, the e ect of serving node intensity on load sharing and achievable throughputs and the e ort to satisfy certain users with high data rate demands are a few problems. Furthermore, the interconnected and complicated problems of service delivery are posed by the spatio-temporal dynamics in service demand and the mobility of User Equipment (UE). This thesis takes a step-by-step approach to solving UA problems in dense and ultra dense HetNets. This research uses stochastic geometry tools, system level simulations, and realistic test case deployment simulations. Models were created for each scenario based on the load balancing, interference coordination, varied densi cation levels, heterogeneity, and user mobility. The work's rst contribution is a solution to the problem of load imbalance and interference coordination. The proposed method is simple to integrate into an existing HetNets network, and the results demonstrate e ective load-aware association and adaptive interference coordination. A cell clustering-based load-aware o setting and an adaptive Low Power Subframe (LPS) approach was developed. The solution allows the separation of UA functions at the UE and network server such that users can make a simple cell-selection decision similar to that in the Maximum Received Signal Strength (max-RSS) based UA scheme, where the network server computes the load-aware o setting and required LPS periods based on the load conditions of the system. The proposed solution was evaluated using system level simulations wherein the results correspond to performance changes in di erent service regions. Results show that the method e ectively solves the o oading and interference coordination problems in dense HetNets. The second contribution of the research is on the coupled and decoupled User Association. It can be used as a guide for network operators to select the appropriate UA scheme for their network. The concepts of Poisson random networks were used to analytiv ically obtain the relative densi cation levels for which we need the o oading, decoupled or coupled UA and validate the analysis with numerical and system level simulation of realistic network. The association window, where users choose to use decoupled association in terms of the relative intensity, transmit powers at each tiers and the Path Loss Exponent (PLE) of the propagation environment, is derived. Further, the ergodic rate expressions in order to study throughput performances in di erent densi cation regions, which can be computed numerically, are formulated . To validate the theoretical analysis, numerical, system level simulation and realistic network analysis were used. The analytical, simulation, and realistic test case results provide insights for the operators about the densi cation ranges, where to use coupled or decoupled association. Finally, the research work focused on solutions for UEs with high data rate demands and mobility management. With Multiple Association (MA), user-centric clustering, control, and user-plane split usages were designed and investigated. Mobility management approaches in Long Term Evolution Advanced (LTE-A)/Fifth Generation (5G) and MA were used. The scheme attempts to separately treat UEs based on their speed by setting some prede ned thresholds. In addition, a clustering approach, which produce virtual cells with which UEs gets associated was developed. Combining of MA with clustering enhances cooperation between most appropriate cells to serve a given UE. The ndings indicate that the issues were addressed in an e cient and e ective manner.
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    Performance Analysis and Optimization in Massive MIMO Systems
    (Addis Ababa University, 2021-03) Amare, Kassaw; Abdelhak, M. Zoubir (Prof.); Dereje, Hailemariam (PhD) Co-Supervisor:
    Next 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.