# 5G Network Management, Orchestration, and Architecture: A Practical Study of the MonB5G project

**Authors:** Hisham A. Kholidy, Mohammed Abuzamak  
**arXiv:** [2212.13747](https://arxiv.org/abs/2212.13747)  
**Date:** 2022-12-28  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2212.13747v1/

## Key points

- Cellular device explosion has opened up opportunities for development in future generations
- Advent of 5G networks brings increased transmission speed, lower latency, and greater capacity for remote execution
- Survey paper titled "5G Network Management, Orchestration, and Architecture: A Practical Study of the MonB5G project" explores benefits of AI for 5G network slicing orchestration and management
- MonB5G project aims to provide zero-touch management and orchestration for network slicing at massive scales in 5G LTE and beyond
- Network slicing allows creation of virtual networks tailored to specific applications or user requirements within a single physical infrastructure
- AI technologies such as machine learning algorithms and intelligent automation can efficiently manage and orchestrate network slicing
- Integration of AI with 5G network slicing enables real-time adaptation to changing network conditions and automates complex tasks
- AI enhances network efficiency by reducing manual intervention
- Findings from the MonB5G project contribute to advancing understanding of how AI can optimize 5G networks while maintaining high performance standards.

## Summary

The cellular device explosion in the past few decades has opened up numerous opportunities for development in future generations. With the advent of 5G networks, there is a significant increase in transmission speed, lower latency, and greater capacity for remote execution. In this survey paper titled "5G Network Management, Orchestration, and Architecture: A Practical Study of the MonB5G project," authors Hisham A. Kholidy and Mohammed Abuzamak delve into the benefits of artificial intelligence (AI) for 5G network slicing orchestration and management. The paper focuses on studying these topics within the context of the EU-funded MonB5G project. This project aims to provide zero-touch management and orchestration to support network slicing at massive scales for 5G LTE and beyond. Network slicing allows for the creation of virtual networks tailored to specific applications or user requirements within a single physical infrastructure. By leveraging AI technologies such as machine learning algorithms and intelligent automation, the authors explore how network slicing can be efficiently managed and orchestrated. This includes optimizing resource allocation, ensuring quality of service (QoS), and enabling dynamic scalability based on demand. The integration of AI with 5G network slicing offers several advantages. It enables intelligent decision-making processes that can adapt to changing network conditions in real-time. Additionally, it enhances network efficiency by automating complex tasks and reducing manual intervention. Overall, this survey paper provides valuable insights into the potential of AI in enhancing 5G network slicing orchestration and management. The findings from the MonB5G project contribute to advancing future generations' understanding of how AI can optimize 5G networks for various applications while maintaining high performance standards.