# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 1 Introduction  
### Background  
The earliest technological solutions in mobile networks aimed at enabling wireless connectivity transfer for cellular data voice mainly, whereas the 5G telecommunication network is hailed as the digitalization and connection of everything and everyone, with the goal of automating much of life [1, 2]. Blind spots remain, however, 95 per cent of the world's population is now living within range of a mobile broadband network, more than 90 per cent of the population owns a mobile phone, and an estimated 4.9 billion people are using the Internet [1]. The data volumes in mobile networks have greatly exceeded the voice volumes, hence the subsequent generations of mobile network systems are being designed to further boost the data rates and network capacities. The International Telecommunication Union (ITU), working towards providing successful telecommunication network deployments at the regional and international levels, established a high-level framework and overall objectives for 5G development [3, 4, 5]. To help meet the ever-increasing demands for wireless communication, the ITU envisioned three main use cases for 5G networks: enhanced mobile broadband (_eMBB_), ultra-reliable and low-latency communications (_URLLC_), and massive machine-type communications (_mMTC_). The main drivers behind this classification address the undergoing trends in mobile networks. Aside from applications generating and requiring a very high data rate,there are expectations of instantaneous connectivity without waiting times (i.e., a single click and an immediate response) and handling a vast number of connected devices simultaneously. Though some need the transmission of high-definition data while on the move, others create stationary congestion. The associated requirements of the supporting 5G system lead to a broad variety of capabilities (Figure 1). For example, _eMBB_ addresses the human-centric use cases for access to multimedia content, services requiring high data rates with improved performance, and an increasingly seamless user experience. The services include video streaming, gaming, or a broad variety of mobile data generated at smart home buildings. _URLLC_ examples include wireless support for industrial manufacturing or production processes, remote medical surgery, transportation safety, etc., aiming toward stringent requirements for capabilities such as throughput, latency, and availability. In general, the profile aids to support industrial automation, mission-critical applications, or self-driving cars. _mMTC_ is characterized by a very large number of connected devices typically transmitting a relatively low volume of nondelay-sensitive data. This profile covers smart city applications, as well as mission-critical connections. Network vendors, supplying telecom operators offering their subscribers 5G with ambitious goals, need to navigate the standardized instruction, according to ITU requirements. The 3 rd Generation Partnership Project (3 GPP), the standard defining 5G technical specifications for telecommunication networks, includes overall 5G network infrastructure architecture [6] and the 5G New Radio (5G NR) concept [7]. In addition to the key architectural aspects, protocols, and interface designs, the standard defines methods for helping to facilitate 5G deployment, inheriting various optimization methods practiced already in the previous mobile networks' generations. The advanced SON (self-organizing network) technology is one example of a solution to enable operators to improve network efficiency. The functionality facilities automated network operations through self-commissioning (RAN node plug and play), self-healing, and self-configuration referring to network autonomous configuration and monitoring, its parameters tuning, and appropriate steering of the network traffic and load, leading to better overall network performance [8].