Project AEGIS (Advanced Efficient Generative Intelligence Simulation)

Project AEGIS (Advanced Efficient Generative Intelligence Simulation)

Developing innovative solutions within the telecommunication domain is becoming increasingly challenging, particularly for AI and machine learning-based approaches. A major hurdle is the lack of access to realistic data. There is not only a scarcity of publicly available, large-scale, and realistic telecom network data, but telecom operators also face significant limitations in utilizing their data due to privacy and security regulations. This project aims to overcome these barriers by creating synthetic yet realistic telecom traffic data using a Generative Adversarial Network (GAN) approach. Our goal as H3M and Turkcell is to develop a live sandbox platform that telecom operators can use for scenario analysis and simulation, and where vendors and application developers can test their solutions.

The project's innovation lies in the concept of 'Selective Realism.' Rather than attempting to generate an exact replica of telecom network traffic, the generative AI will focus on producing realistic data tailored to specific use cases. This targeted approach will significantly reduce the computational resources and time required, while still generating data that is highly relevant and useful. The simulation platform will be capable of generating on-demand data with flexibility both laterally and vertically. For instance, it can generate traffic data for a specific base station coverage area or produce minute-by-minute network demand data for a large network without generating the payload.

Collaboration between project partners is crucial for identifying high-value use cases and developing a versatile GAN software platform applicable to a wide range of applications. Examples of these applications include:

• Detection of anomalies such as DDoS attacks
• Network capacity planning
• Missing KPI generation
• Network parameter optimization
• Disaster planning
• Network security and weakest link analysis

The project is anticipated to span 36 months.

SME, Vendor, Operator, Academy