Abstract:
The Fifth-generation (5G) wireless represents a significant advancement for mo- bile connectivity successfully, near-instant response times, the ability to connect a huge number of devices reliably. But this power comes with a catch: managing its radio resources is incredibly complex, thanks to constantly shifting network con- ditions and wildly different user demands. How can the vital resources of a 5G network effectively managed? Question by applying machine learning techniques to the problem explored by this thesis. In order to replicate both the core and radio access network, started by creating a simulated 5G environment with open-source platforms Open5GS, Free5GC, and UERANSIM. with the testable place, we are able to systematically trial and analyze various resource allocation methods. To improve resource allocation performance, were applied some machine learning ap- proaches as like Linear Regression, Polynomial Regression, and XGBoost. In order to measure how well each model performed, Metrics including throughput, latency, and spectrum efficiency considered for evaluation. More accurate predictions in the rapidly changing conditions of 5G networks suggested by XGBoost, achieving a noticeably higher R2 value and showed the strongest among all the models. In 5G systems, that machine learning can help enhance resource management indicated by this study’s results. They give support towards realizing more efficient and adaptive management of networks by facilitating smarter and more flexible decisions. Overall, this study contributes to development solutions for next-generation mobile communication technologies based on AI and analytics-based.
