Project Context
Context
Networks
Networks have evolved significantly from traditional static infrastructures to more dynamic, intelligent, and adaptive systems.
5G and Beyond-5G networks must:
- handle vast amounts of data.
- support a diverse range of applications.
- ensure high reliability and low latency.
Goal: Achieve self-managing networks, where human intervention is minimized.
NWDAF
Network Data Analytics Function are being developed with the goal of:
- collecting and analyzing network data
- provide predictions for network optimization
Has three main aspects:
- Data Collection
- Analytics Processing
- Analytics Exposure
Goal: Automating the 5G network with machine learning and data analytics
MLOPs Pipeline
- MLOps is an extension of DevOps, specifically adapted for machine learning workflows.
- End-to-end machine learning development process.
- Aims to unify the release cycle for machine learning.
- Enables the application of agile principles to machine learning projects.
Problem
- Increase in data consuming slows the network.
- Utilization spikes, can compromise network QoS.
- Technical problems (latency, packet loss) cause data transmission delays.
- Simply setting up more network and maintaining it is expensive.
Goals
- Implement a Data Pipeline for Network Intelligence.
- Develop and Integrate Machine Learning Models.
- Automate Network Optimization & Decision-Making.
- Implementation of a scalable and modular MLOps pipeline that works as a NWDAF when integrated with a 5G network.
- Ensure Compliance with 3GPP Standards.
- Attempt Integration with existing 5G Core Network Components.
- Ensure seamless Communication Between Services within the pipeline.
- Evaluate System Performance under several Network Conditions.
- Provide a User-Friendly Deployment and Monitoring System.
Expected Results
- A modular and scalable MLOps pipeline that when integrated with a 5G network works as a base for a NWDAF.
- A NWDAF-like architecture that can be deployed in any 3GPP-compliant network.
- Comprehensive validation of NWDAF-like architecture performance through real-world use cases in a 5G environment.