Energy-efficient power allocation for downlink non orthogonal multiple access networks based on game theory and genetic algorithm / Reem Mustafa Mah’d Al Debes

Reem Mustafa , Mah’d Al Debes (2025) Energy-efficient power allocation for downlink non orthogonal multiple access networks based on game theory and genetic algorithm / Reem Mustafa Mah’d Al Debes. PhD thesis, Universiti Malaya.

	PDF (The Candidate's Agreement) Restricted to Repository staff only Download (123Kb)
	PDF (Thesis PhD) Download (2044Kb)

Abstract

The exponential growth in the number of users and their increasingly diverse demands in next-generation wireless networks has created significant challenges in managing limited resources while ensuring energy-efficient communication. The need to meet the quality of service (QoS) requirements for this rapidly expanding user base, particularly with heightened data rate expectations, underscores the urgency for innovative solutions. Although 5G and beyond technologies provide a foundation for next-generation networks, further advancements are required to improve energy efficiency (EE) and spectrum efficiency (SE) to meet these demands. This study focuses on optimizing energy-efficient power allocation in Non-Orthogonal Multiple Access (NOMA) systems, a transformative approach that allows multiple users to share resources simultaneously. The research leverages Artificial Intelligence (AI)-based Genetic Algorithms (GA) and game theory to address critical challenges in resource allocation. GA is specifically chosen for its ability to solve complex, non-linear problems by efficiently navigating large solution spaces. Complementing this, game theory offers a robust framework to model strategic interactions among users, ensuring fair and effective resource distribution. Together, these methods tackle critical gaps in resource allocation, including the trade-off between energy efficiency and data rate, and the challenges posed by both perfect and imperfect channel state information (CSI). The novel power allocation mechanism developed in this study demonstrates significant improvements. The proposed method achieves a 75% enhancement in energy efficiency compared to conventional Orthogonal Multiple Access (OMA) and an 11% improvement over benchmark NOMA algorithms. Additionally, it reduces outage probability by 25% and 10% relative to OMA and existing NOMA algorithms, respectively. These results validate the algorithm's robustness, particularly under imperfect CSI conditions, where traditional methods often fail. Furthermore, the research explores advanced applications such as integrating NOMA with Millimeter-Wave technology and optimizing user association strategies, enhancing system capacity and overall performance. The findings highlight the pivotal role of Genetic Algorithms and game theory in overcoming the limitations of conventional resource allocation methods. The integration of these advanced techniques ensures adaptability, efficiency, and resilience in dynamic network environments. By achieving substantial gains in energy efficiency and data rates, this study sets a new benchmark for resource allocation strategies in 5G and beyond networks. The proposed method demonstrates how AI-driven solutions, coupled with strategic modeling frameworks like game theory, can address the pressing challenges of next-generation wireless communication systems effectively.

Actions (For repository staff only : Login required)