Resource Allocation in 5G Networks - Machine Learning Approach

Abstract

The influx of fifth generation [5G] technology marks the commencement of a fresh era in wireless communication, offering unparalleled data speeds, extremely low latency and extensive device connectivity. Realizing the full potential of 5G, efficient resource allocation strategies that optimize the use of the available spectrum is studied. This research paper proposes an efficient approach to optimize resource allocation in 5G networks by leveraging the power of machine learning (ML). We probe into the challenges of traditional resource allocation methods and present a novel idea ML-driven approach that adapts and learns from network dynamics to predict the allocation of resources dynamically. The utilization of ML models including supervised learning, enables the system to anticipate the improvisation of performance of 5G network, enhance spectral efficiency, and improve quality of service (QoS). This research offers valuable insights into the role of ML in 5G networks. In the study, we have analyzed and evaluated each model against key metrics such as mean square value (MSE), R2 values. It is found that the real time captured data exhibit efficiency. Finally, we discuss future research directions and possible applications of ML to optimize resource allocation in 5G networks.