# Optimized Architecture for Efficient OFDMA Network Design
###### Abstract  
This study presents a novel approach to enhancing the design and performance of OFDMA (Orthogonal Frequency Division Multiple Access) networks, with a particular focus on WiMAX (Worldwide Interoperability for Microwave Access) for Best Effort (BE) services. The proposed method integrates a robust Markovian analytical model with four advanced scheduling algorithms: throughput fairness, resource fairness, opportunistic scheduling, and throttling. A sophisticated simulator was developed, incorporating an ON/OFF traffic generator, user-specific wireless channels, and a dynamic central scheduler to validate the model's accuracy and evaluate its robustness by dynamically allocating radio resources per frame. The validation study showed that the proposed model reduced simulation time by over 90%, completing analytical calculations in just 15 min, compared to nearly 2 days for simulations using conventional scheduling algorithms. Performance metrics such as the average number of active users and resource utilization closely matched those from the validation study, confirming the model's accuracy. In the robustness study, the model consistently performed well across diverse traffic distributions (exponential and Pareto) and channel conditions. The proposed architecture increased network throughput by up to 25% and reduced latency under dynamic conditions, demonstrating its scalability, adaptability, and efficiency as a crucial solution for next-generation wireless communication systems. O 2024  
of modern networks [10, 11]. In response, more intelligent and adaptive scheduling algorithms have been developed to efficiently manage resources and enhance Quality of Service (QoS), particularly in OFDMA networks [12]. Resource allocation remains a critical factor in maintaining system performance, especially in wireless networks where user demands and channel conditions are constantly shifting [13, 14]. Numerous studies have proposed advanced scheduling algorithms to improve system capacity, fairness, and overall network performance [15, 16, 17]. However, achieving an optimal balance between these objectives continues to be a challenge, particularly in networks with variable traffic loads and unpredictable channel conditions [18, 19, 20]. This study introduces a novel approach by integrating a Markovian analytical model with four advanced scheduling algorithms (throughput fairness, resource fairness, opportunistic scheduling, and throttling) designed specifically for WiMAX networks. The proposed model enhances resource allocation by considering the complex interactions between user traffic, channel variability, and QoS requirements. A robust simulator, incorporating an ON/OFF traffic generator, individual wireless channels for each user, and a central scheduler, is used to validate the model and assess its performance in realistic network scenarios. By relaxing the assumptions of the analytical model and testing it under dynamic conditions, the study aims to demonstrate the model's ability to maintain network efficiency and scalability. This approach adapts to changing traffic loads and channel conditions, ensuring that WiMAX networks can reliably support BE services even under fluctuating conditions. The insights from this research offer significant contributions to the development of more efficient, scalable, and adaptable network architectures. By optimizing resource management and integrating advanced scheduling algorithms, this study positions WiMAX as a critical component in the next generation of broadband solutions. The findings also support the broader goal of advancing wireless communication technologies, enabling future networks to meet the growing demands of an increasingly connected world. The remainder of this paper is structured as follows: Section 2 provides a detailed review of the related work, highlighting the key contributions and gaps in the existing literature. Section 3 presents the analytical model for WiMAX network dimension, based on Markov chain theory, focusing on Best Effort (BE) services.