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

arXiv: https://arxiv.org/abs/2601.07646 · HTML: https://mlsanity.com/paper/2601.07646/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 51 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [Does Training with Synthetic Data Truly Protect Privacy?](https://mlsanity.com/paper/2502.12976/) (2025)
- [PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN](https://mlsanity.com/paper/1911.12046/) (2019)
- [Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs](https://mlsanity.com/paper/1811.12397/) (2018)
- [When Machine Learning Meets Big Data: A Wireless Communication Perspective](https://mlsanity.com/paper/1901.08329/) (2019)
- [Transfer learning for time series classification using synthetic data generation](https://mlsanity.com/paper/2207.07897/) (2022)
- [Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks](https://mlsanity.com/paper/2005.08281/) (2020)
- [AI/ML-based Load Prediction in IEEE 802.11 Enterprise Networks](https://mlsanity.com/paper/2310.07467/) (2023)
- [Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs](https://mlsanity.com/paper/2203.10472/) (2022)
- [Towards an AI/ML-defined Radio for Wi-Fi: Overview, Challenges, and Roadmap](https://mlsanity.com/paper/2405.12675/) (2024)
- ["It's Your Turn": A Novel Channel Contention Mechanism for Improving Wi-Fi's Reliability](https://mlsanity.com/paper/2410.07874/) (2024)

## Abstract (arXiv)

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.