Adaptive Resource Allocation Framework with Advanced Technologies for Enhanced 5G Mobile Network Performances
Y Meghamala, Pulipati John Paul, Vivek Kumar. M · 2025
The 5G networks are capable of executing a wide range of applications. Some of these applications are improved mobile broadband, massive Machine Type Communications (mMTC), and ultra-reliable low-latency communication (URLLC). In order to satisfy these requirements adequately, resource management is essential. This work develops an approach of dynamic resource allocation with an emphasis on the use of cutting-edge technologies like edge computing, network slicing, AI, and ML for 5G enhancement. The system ensures dynamic resource allocation strategies which are aimed at reducing the service latency, conserving energy and improving the throughput of the system. This is done by leveraging user requests, real-time traffic patterns, and service quality indicators. The reduction in end-to-end latency of 25%, a 30% increase in energy efficiency and improved diversity of resource application are some of the quantitative improvements on QoS metrics that were achieved. Through simulations, the benefits were demonstrated. The results of this study suggest that the growing complexity of today's communication networks could be addressed by processes that are adaptive to the dynamics of advanced technologies.