Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)

TL;DR

This paper reviews the integration of machine learning into 5G standalone networks, highlighting its role in enhancing network operations, enabling new use cases like URLLC, and transforming 4G to next-generation 5G technology.

Contribution

It provides an overview of how machine learning is incorporated into 5G SA core networks and its impact on network efficiency and new cellular applications.

Findings

ML improves network throughput and reliability.

ML enables ultra-reliable low latency communications (URLLC).

ML integration facilitates transition from 4G to 5G networks.

Abstract

Machine learning (ML) is included in Self-organizing Networks (SONs) that are key drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities. It is included in the 5G Standalone (SA) system is one of the 5G communication tracks that transforms 4G networking to next-generation technology that is based on mobile applications. The research's main aim is to an overview of machine learning (ML) in 5G standalone core networks. 5G Standalone is considered a key enabler by the service providers as it improves the efficacy of the throughput that edges the network. It also assists in advancing new cellular use cases like ultra-reliable low latency communications (URLLC) that supports combinations of frequencies.