Improving 5G/B5G Network Performance with RFID-Enabled Resource Management Systems

In the rapidly evolving landscape of 5G and B5G (beyond 5G) networks, efficient resource optimization is critical to addressing the escalating demands for high-speed, low-latency, and energy-efficient communication. This study explores the integration of Radio Frequency Identification (RFID) technology as a novel approach to enhance resource management in 5G/B5G networks. The motivation behind this research lies in overcoming persistent challenges such as spectrum congestion, high latency, and inefficient load balancing, which impede the performance of traditional resource allocation methods. To achieve this, RFID tags were embedded in critical network components, including user devices, base stations, and Internet of Things (IoT) nodes, enabling the collection of real-time data on device status, location, and resource utilization. RFID readers strategically placed across the network continuously captured this data, which was processed by a centralized controller using a custom-designed optimization algorithm. This algorithm dynamically managed key network resources, including spectrum allocation, load balancing, and energy consumption, ensuring efficient operation under varying network conditions. Simulations were conducted to evaluate the performance of the RFID-based model against traditional 4G dynamic resource allocation techniques. The results demonstrated substantial improvements in key performance metrics. The proposed system achieved a 25 % increase in spectrum utilization, a 30 % reduction in average latency, a 15 % boost in network throughput, and a 20 % decrease in overall energy consumption. These gains highlight the effectiveness of the RFID-based optimization model in meeting the stringent performance requirements of 5 G networks, particularly in high-density deployments. This study provides a scalable, cost-effective solution for optimizing resource management in 5G/B5G and lays the groundwork for future advancements in 6G networks. By leveraging real-time data and intelligent resource allocation, the proposed model addresses critical challenges in modern communication systems, ensuring enhanced network efficiency, reliability, and sustainability.