GAN Synthetic Data Generation for 5G NWDAF Analytics
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Solution Overview
Problem
Modern wireless communication networks face challenges in obtaining and utilizing high-quality, realistic network traffic data for training Machine Learning (ML) models, due to difficulties in collecting, labeling, and sharing real data, which is often outdated and privacy-constrained.
Innovation Solution
The implementation of a Generative Adversarial Network (GAN) within the Network Data Analytics Function (NWDAF) to generate synthetic network traffic data that mimics actual network traffic, addressing the limitations of real data by providing timely, voluminous, and privacy-compliant training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real network traffic data is collected and used for training ML models, then the training data reflects actual network conditions, but privacy concerns and data sharing limitations arise
Solution Approach 1:
The patent creates synthetic copies of real network traffic data using GANs. The generator network learns the distribution patterns of real traffic data and generates synthetic samples that preserve statistical characteristics while removing sensitive information. This allows ML models to be trained on realistic data without exposing actual user privacy.
Solution Approach 2:
The GAN framework acts as an intermediary between real data and training requirements. The generator and discriminator networks work together to transform real traffic patterns into synthetic data, mediating the conflict between using real data for accurate training and protecting privacy by not directly exposing sensitive information.
2Reliability
If real network traffic data is collected for ML training, then the data is up-to-date and relevant, but difficulties in collection, labeling, and sharing arise
Solution Approach 1:
The GAN system performs self-service by automatically learning data distribution patterns from real traffic and generating synthetic samples without manual intervention. The generator network autonomously creates labeled training data by synthesizing traffic flows that inherit statistical properties from real data, eliminating the need for manual data collection and labeling efforts.
Solution Approach 2:
Instead of manually collecting and labeling real data, the system creates synthetic copies through the GAN generator. These copied data samples preserve the essential characteristics and label distributions of real traffic while requiring no manual collection or annotation processes.
3Quantity of substance
If real network traffic data is used for training, then the training data volume can be sufficient, but scalability and continuous availability are limited
Solution Approach 1:
The GAN model enables continuous generation of training data on-demand. Once trained on real traffic patterns, the generator can continuously produce synthetic data without requiring ongoing access to real networks, ensuring unlimited availability of training data for continuous ML model retraining and adaptation.
Solution Approach 2:
The system creates unlimited synthetic copies of training data from the learned distribution. A single training phase on real data enables the generator to produce as many synthetic samples as needed, scaling training data volume without additional data collection efforts.
Data Source
AI summary
A Generative Adversarial Network (GAN) is used to generate synthetic network traffic data, such as for use in training Machine Learning (ML) models. In one embodiment, a new NWDAF analytic “SyntheticData” is defined. The analytic receives as input from a requesting network function (NF) at least an amount of network traffic data requested and the type of network traffic data requested. The SyntheticData analytic uses a GAN model to generate realistic synthetic network traffic data based on actual network traffic collected in the wireless communication network. The analytic sends to the requesting NF the specified amount of synthetic network traffic data of the specified type. In one embodiment, the synthetic network traffic data generation is implemented as a new logical function of an NWDAF: the Data Generator Logical Function (DGLF).