Optimizing Resource Allocation for 5G Internet-of-Things Networks Using Machine Learning Techniques

The development of smart devices and the enhancement of the 5G network pose a challenge in the management of resources especially in the dynamic and high-density networks. This paper proposes a machine learning approach for the optimization of resource allocation for 5G Internet-of-things (IoT) networks. The framework integrates real-time data processing, dynamic routing and edge computing for the improvement of the network throughput and quality of service (QoS). The suggested methods have been developed and evaluated in MATLAB simulations have shown important enhancements in latency, bandwidth and energy efficiency. Through the overcoming the limitations of the conventional methods, this research offers the solutions for the resource allocation problem in the 5G-IoT networks which are scalable, adaptive and efficient.