# Optimized Architecture for Efficient OFDMA Network Design
## 2 Related Works  
In recent years, various studies have employed analytical models and optimization techniques to enhance the efficiency and reliability of different systems. One notable example is the use of state-based Markov models in the performance analysis of IoT systems, as presented in [21]. This study developed a time-dependent performance analysis of a smart trash bin system, considering multiple hardware components (Arduino Uno, ultrasonic sensors, servo motors, switches, batteries, and jumper wires) while evaluating system reliability, unreliability, and Mean Time to Failure (MTTF). By leveraging a state-based Markov model, the authors aimed to predict failure points and optimize maintenance schedules, ensuring the longevity and reliability of the smart trash bin. Sensitivity analysis identified critical components (e.g., the servo motor and switch) that significantly impact performance, guiding targeted interventions for optimal operation. This approach demonstrates how analytical modeling when combined with reliability metrics, can optimize IoT system performance under real-world conditions. While the smart trash bin study effectively enhances IoT system reliability, it is limited to static components and does not address the dynamic nature of wireless communication networks. In contrast, the current work employs a Markovian framework tailored to optimize resource allocation in OFDMA networks. This research focuses on real-time adaptations to fluctuating traffic and user demands, which are crucial for maintaining high performance in modern wireless systems. Another significant study focuses on fault tolerance in wireless networks, particularly within IoT sensor networks. The paper [22] investigates methods for enhancing the reliability of sensors in cluster-based wireless sensor networks (WSNs). It employs Markov models to analyze fault tolerance in nodes that alternate between active sessions and waiting periods, evaluating sensor availability and overall network reliability, with an emphasis on power management strategies such as battery redundancy. The findings highlight how cluster-based approaches can extend the operational lifetime of sensor networks by improving fault tolerance and ensuring uninterrupted data collection. While this IoT study emphasizes fault tolerance and power management in sensor nodes, it primarily targets specific components rather than broader network performance. The current work expands on this by utilizing Markov models to optimize resource allocation in OFDMA networks, prioritizing metrics such as throughput and latency. This focus on dynamic resource management allows for more responsive network behavior, thereby improving overall communication performance under varying traffic conditions. Recent research has directed substantial efforts toward optimizing the performance of OFDMA networks, particularly with novel technologies. For instance, study [23] explores using reconfigurable intelligent surfaces (RIS) to enhance energy efficiency in OFDMA networks. By jointly optimizing RIS reflection coefficients and OFDMA resource allocation, the authors aim to maximize energy efficiency while ensuring quality-of-service (QoS) requirements. Their approach employs a sub-optimal solution based on alternating optimization (AO) to address the complex problem of resource allocation in RIS-enabled networks, demonstrating significant improvements in energy efficiency compared to conventional OFDMA systems without RIS. However, this study does not address critical aspects of user demand and traffic patterns. The current work tackles these limitations by implementing advanced scheduling algorithms that maximize throughput and fairness among users in WiMAX networks. This ensures that network resources are utilized optimally, which is vital for maintaining quality of service in diverse scenarios. Several works in the field of OFDMA network simulation have sought to improve resource allocation efficiency.