Saudi Cultural Missions Theses & Dissertations
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Item 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