Studying the Role of Synthetic Data for Machine Learning-based Wireless Networks Traffic Forecasting

TL;DR

This paper introduces a novel synthetic data generation method for Wi-Fi traffic forecasting that requires minimal real data, enhances model accuracy, and offers scalable, privacy-friendly solutions for wireless network management.

Contribution

It presents a new approach based on auto-regressive noise for generating realistic Wi-Fi traffic data with minimal real data, improving forecasting accuracy and generalization.

Findings

Models trained on synthetic data achieve MAE within 10-15 of real data models.

Synthetic data improves prediction accuracy by up to 50% in generalization scenarios.

The method requires minimal real data and scales efficiently for large deployments.

Abstract

Synthetic data generation is an appealing tool for augmenting and enriching datasets, playing a crucial role in advancing artificial intelligence (AI) and machine learning (ML). Not only does synthetic data help build robust AI/ML datasets cost-effectively, but it also offers privacy-friendly solutions and bypasses the complexities of storing large data volumes. This paper proposes a novel method to generate synthetic data, based on first-order auto-regressive noise statistics, for large-scale Wi-Fi deployments. The approach operates with minimal real data requirements while producing statistically rich traffic patterns that effectively mimic real Access Point (AP) behavior. Experimental results show that ML models trained on synthetic data achieve Mean Absolute Error (MAE) values within 10 to 15 of those obtained using real data when trained on the same APs, while requiring significantly less training data. Moreover, when generalization is required, synthetic-data-trained models improve prediction accuracy by up to 50 percent compared to real-data-trained baselines, thanks to the enhanced variability and diversity of the generated traces. Overall, the proposed method bridges the gap between synthetic data generation and practical Wi-Fi traffic forecasting, providing a scalable, efficient, and real-time solution for modern wireless networks.