Saudi Cultural Missions Theses & Dissertations
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Item Restricted Design and Analysis of Next Generation Wireless Networks(Saudi Digital Library, 2025) Altuwairgi, Khaled Humaid; Hamdi, KhairiRecent advancements in wireless communications have increased the demand for high data rates, massive connectivity, high spectral and energy efficiency, and low latency, which cannot be met by existing systems. The sixth-generation (6G) wireless network is envisioned as the next step to support these demands by integrating technologies, including intelligent reflecting surface (IRS), backscatter communication, non-orthogonal multiple access (NOMA), integrated sensing and communication (ISAC), and terahertz (THz) communications. Specifically, the IRS enhances the energy and cost efficiency by controlling the propagation environment through an array of reflecting elements. Backscatter communication enables passive battery-free devices to communicate using external RF signals, offering an energy-efficient and low-cost solution for the Internet of Things (IoT) paradigm. NOMA improves spectral efficiency and massive connectivity by allowing multiple users to share the same time-frequency resources, while ISAC combines sensing and communication functionalities into a single system for efficient spectrum and hardware usage. Finally, THz communication addresses the current limited spectrum by providing extensive bandwidth that supports ultra-high data rates. This thesis studies the integration of these technologies with a special focus on IRS and backscatter communications, considering various system models and realistic scenarios. It evaluates the performance of IRS-aided backscatter communication in both dedicated and ambient configurations using different detection techniques and transmission schemes. It also investigates IRS-assisted THz to serve multiple users through NOMA and wireless powered communication under various practical scenarios. Moreover, it explores the integration of ISAC with ambient backscatter communication. The thesis identifies the potential benefits of these technologies and examines the adverse impacts of practical factors such as beam misalignment, co-channel interference, imperfect successive interference cancellation, phase shift quantization errors, and hardware imperfection. Accurate analytical expressions are developed for key metrics, including bit error rate, ergodic capacity, and outage probability, under various system models and transmission schemes. Numerical and simulation results are provided to validate the accuracy of the theoretical analysis and provide valuable insights into the system design.14 0Item Restricted Machine Learning Empowered Resource Allocation for NOMA Enabled IoT Networks(Saudi Digital Library, 2023-12-20) Alajmi, Abdullah Saad; Nallanathan, ArumugamThe Internet of things (IoT) is one of the main use cases of ultra massive machine type communications (umMTC), which aims to connect large-scale short packet sensors or devices in sixth-generation (6G) systems. This rapid increase in connected devices requires efficient utilization of limited spectrum resources. To this end, non-orthogonal multiple access (NOMA) is considered a promising solution due to its potential for massive connectivity over the same time/frequency resource block (RB). The IoT users’ have the characteristics of different features such as sporadic transmission, high battery life cycle, minimum data rate requirements, and different QoS requirements. Therefore, keeping in view these characteristics, it is necessary for IoT networks with NOMA to allocate resources more appropriately and efficiently. Moreover, due to the absence of 1) learning capabilities, 2) scalability, 3) low complexity, and 4) long-term resource optimization, conventional optimization approaches are not suitable for IoT networks with time-varying communication channels and dynamic network access. This thesis provides machine learning (ML) based resource allocation methods to optimize the long-term resources for IoT users according to their characteristics and dynamic environment. First, we design a tractable framework based on model-free reinforcement learning (RL) for downlink NOMA IoT networks to allocate resources dynamically. More specifically, we use actor critic deep reinforcement learning (ACDRL) to improve the sum rate of IoT users. This model can optimize the resource allocation for different users in a dynamic and multi-cell scenario. The state space in the proposed framework is based on the three-dimensional association among multiple IoT users, multiple base stations (BSs), and multiple sub-channels. In order to find the optimal resources solution for the maximization of sum rate problem in network and explore the dynamic environment better, this work utilizes the instantaneous data rate as a reward. The proposed ACDRL algorithm is scalable and handles different network loads. The proposed ACDRL-D and ACDRL-C algorithms outperform DRL and RL in terms of convergence speed and data rate by 23.5\% and 30.3\%, respectively. Additionally, the proposed scheme provides better sum rate as compare to orthogonal multiple access (OMA). Second, similar to sum rate maximization problem, energy efficiency (EE) is a key problem, especially for applications where battery replacement is costly or difficult to replace. For example, the sensors with different QoS requirements are deployed in radioactive areas, hidden in walls, and in pressurized pipes. Therefore, for such scenarios, energy cooperation schemes are required. To maximize the EE of different IoT users, i.e., grant-free (GF) and grant-based (GB) in the network with uplink NOMA, we propose an RL based semi-centralized optimization framework. In particular, this work applied proximal policy optimization (PPO) algorithm for GB users and to optimize the EE for GF users, a multi-agent deep Q-network where used with the aid of a relay node. Numerical results demonstrate that the suggested algorithm increases the EE of GB users compared to random and fixed power allocations methods. Moreover, results shows superiority in the EE of GF users over the benchmark scheme (convex optimization). Furthermore, we show that the increase in the number of GB users has a strong correlation with the EE of both types of users. Third, we develop an efficient model-free backscatter communication (BAC) approach with simultaneously downlink and uplink NOMA system to jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using a reinforcement learning algorithm, namely, soft actor critic (SAC). With the advantage of entropy regularization, the SAC agent learns to explore and exploit the dynamic BAC-NOMA network efficiently. Numerical results unveil the superiority of the proposed algorithm over the conventional optimization approach in terms of the average sum rate of uplink backscatter devices. We show that the network with multiple downlink users obtained a higher reward for a large number of iterations. Moreover, the proposed algorithm outperforms the benchmark scheme and BAC with OMA in terms of sum rate, self-interference coefficients, noise levels, QoS requirements, and cell radii.23 0