Saudi Cultural Missions Theses & Dissertations
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Item Restricted Design of Polarisation Reconfigurable Millimeter-Wave MIMO Antenna(University of Sheffield, 2024-09-11) Alharbi, Faisal; Khamas, SalamA 2×2 multiple-input multiple-output (MIMO) of four reconfigurable loops antenna for millimeter-wave (mmWave) communication systems is proposed. The antenna system supports three polarisation modes and is designed to operate at 28 GHz. Polarisation reconfigurability is achieved by electrically switching two PIN diodes. For both circular polarisation senses, a total efficiency of approximately 76% and a maximum gain of around 7 dBic are obtained at the operated frequency. Additionally, the antenna provides linear polarisation (LP) with a total efficiency of 65% and a maximum gain of 6.1 dBic when the two PIN diodes are unbiased. The envelope correlation coefficient (ECC) is found to be less than 0.002 with a diversity gain (DG) greater than 9.9 at 28 GHz. With an overall size of 30 mm × 30 mm, the proposed MIMO antenna features a small footprint and simple planar configuration.19 0Item Restricted QUALITY OF SERVICE AWARE DYNAMIC WAVELENGTH AND BANDWIDTH ALLOCATION ALGORITHM FOR 5G FRONTHAUL NETWORKS(Universiti Teknologi Malaysia, 2024-01-01) Alsheibi, Abdullah Zaini Zaini; Muhammad Bin Muhammad Al Farabi, IqbalThe Quality of Service (QoS) requirements for 5G are very strict in terms of latency of Fifth Generation (5G) traffic. 5G fronthaul networks between the baseband unit and remote radio heads are planned to use Next Generation – Passive Optical Networks 2 (NG-PON2) to carry their traffic. However, these Time Wavelength Division Multiplexing (TWDM) PON based networks also provide service to other traffic such as residential and corporate users. Therefore, it is crucial to provide QoS guarantees to 5G traffic. The main issue includes allocating transmission over multiple wavelengths while maintaining QoS. Although using all the available wavelengths all the time may seem to be the simple solution, each active wavelength increases the cost of operation. To solve these issues, this work proposes a technique using fuzzy logic to dynamically allocate wavelength and bandwidth for 5G fronthaul networks. Two types of traffic, i.e., 5G traffic and normal traffic are considered while the problem of Dynamic Wavelength and Bandwidth Allocation (DWBA) is formulated as a queueing theory problem using penalties and a cost function. Penalties are assigned for waiting packets as well as each active wavelength. Then, modelling the number of active wavelengths, the number of packets in the system, the total cost, and the traffic intensity as input fuzzy variables while modelling the change in the number of active wavelengths as the fuzzy output variable. The Mamdani implication is used for the fuzzy inference engine and the height method is used for defuzzification of the output variable. Simulations in MATLAB show that the proposed technique can maintain the latency of 5G traffic lower than the defined threshold while also significantly reducing the number of active wavelengths. A comparison with the existing state-of-the-art techniques shows that the proposed technique results in an improvement of at least 46% and 29% in terms of the average number of active wavelengths and the average latency for 5G traffic, respectively.24 0Item Restricted Low Cost Transparent and Flexible Antenna for Next Generation Communication Networks(University of Illinois at Chicago, 2024-02-09) Alsaab, Nabeel; Chen, Pai-YenNext-generation antenna design plays a vital role in enabling technologies in fifth generation (5G) and beyond fifth-generation (B5G) wireless networks. 5G and B5G technologies are envisioned to provide ubiquitous connectivity, enhanced coverage, ultra reliable low latency, and high data rates to meet societal and industrial needs. Furthermore, they are envisioned to unleash the potential of machine-to-machine communication and internet-of-things (IoTs) to build an ecosystem where networks can provide instantaneous connectivity for billions of connected devices. However, one challenge to the wide deployment of such a level of connectivity is that traditional antennas used in the current 4G and 4G-LTE systems are often large and intrusive. The exponential growth in demand for IoT devices, gateways and other wireless modulus, alongside with aesthetics requirement and cost considerations, have driven engineers to research “invisible” optically- transparent antennas and arrays that can be used in access points and signal repeaters embedded into existing urban infrastructure, without spoiling the aesthetic appearance of the environment and architectures. This thesis focuses on development of robust, cost-effective, and ecologically acceptable nanomaterials for optically transparent antennas, intelligent surfaces, and radio-frequency (RF) devices. Particularly, a large- area, ultralow-profile and mechanically-flexible transparent conductive films (TCFs) based on the metal-dielectric nanocomposite (MDNC) will be used to build these key component in next- generation communication systems. Moreover, the optimal design of MDNC, which exhibits high optical transparency and decently low electrical resistivity, will be conducted using the optical nanocircuit theorem and transfer matrix method. The versatility of the MDNC-TCF is demonstrated by implementing various transparent and flexible antennas, in the form of omnidirectional linear dipole, unidirectional Yagi-Uda antenna, microstrip patch antenna, and novel solar-powered body-wearable antenna. This research also studies transparent metasurfaces and their applications in antenna radomes, which can be used in, for example, high-gain and low- RCS Fabry-Perot cavity antennas and solar-powered base station antenna. The results of this thesis will pave the way for the practical realization of low-cost, conformal and optically- transparent antennas and intelligent surfaces that are capable of enhancing and optimizing connectivity of 5G and B5G communication systems.19 0Item Restricted Energy Efficient D2D Communications Underlay Future Wireless Networks(University of Exeter, 2023-05-09) Alenezi, Sami Mohammed L; Min, Geyong; Luo, ChunboFrom the first generation of mobile networks to the present, the demand for more network bandwidth and energy has grown significantly as a result of the growth in users and applications. In the future, there will be billions of heterogeneous connected devices requiring high-quality network services. The demands of these cellular users are difficult to be satisfied by the technologies currently available particularly due to the limited spectrum resources. Device to Device (D2D) communication is a potential strategy for improving device performance by allowing direct communication between user pairs that are close to each other. Reducing network latency, decreasing energy consumption, increasing throughput, and improving coverage area are potential advantages of using D2D communications. However, key problems may arise when operating D2D communications in cellular networks to directly or indirectly affect energy and spectrum efficiency, for example, the interference problems between D2D devices, the interference between D2D devices and cellular devices, device discovery problems, and mode selection problems. Although traditional techniques have been proposed to solve such problems, device position, power transmission, and channel conditions are typically dynamic, particularly in the future dynamic cellular network environment. Because traditional optimisation techniques are facing increasing difficulty in rapidly changing environments, machine learning techniques become a promising tool for effective resource allocation and interference management. From this standpoint, this thesis aims to propose methods based on machine learning in order to increase the energy efficiency of D2D-assisted cellular networks. The contributions from the Machine Learning view are that the state space, action space and reward function are defined in a distributed and centralised manner to further specify the problem and use the reinforcement learning-based method to maximise energy efficiency. To be more specific, the key contributions in this thesis are listed as follows: - Few studies have been conducted to investigate the impact of user mobility on energy and spectrum efficiency of D2D communications. The effect of user mobility on the energy efficiency of D2D communications in the high-speed scenario has not been thoroughly studied especially in the state-of-the-art research in which user speed is considered very low. Thus, more research is needed to explain how D2D performance could be improved in dynamic scenarios. This thesis investigates 1) the impact of mobility on D2D communication in order to better understand the operational efficiency of next-generation cellular network-assisted D2D technologies; 2) the potential of Machine Learning (ML) algorithms to mitigate the negative impact of unpredictable user mobility; and 3) the performance gain of the proposed methods over other ML and more traditional methods. - The thesis further studies the energy efficiency of D2D communications in cellular networks. In particular, it proposes a centralised power control algorithm based on reinforcement learning to optimise energy usage while minimising interference to cellular users in order to maintain the Quality of Service (QoS). The centralised power control algorithm is hosted at the base station. Compared to the benchmark algorithms, simulation results show that the proposed method can effectively increase system energy efficiency while maintaining cellular user QoS. - Moreover, to optimise resource allocation and improve energy efficiency, the thesis proposes a Proximal Policy Optimisation (PPO)-based joint channel selection and power allocation scheme based on the Markov Decision Process (MDP). Channel selection and power allocation are jointly considered with the aim to maximise the overall energy efficiency of the network while guaranteeing the minimum requirement of QoS. Extensive simulation experiments have been carried out to validate the effectiveness of the proposed method. In terms of energy efficiency and training time, the results show that the proposed method outperforms other existing algorithms.21 0