Diffusion Models for Wireless Channel Generation and Augmentation
Dr. Anna Kowalski, Prof. Robert Heath
NYU Wireless
Abstract
We introduce a diffusion-based generative model for synthesizing realistic wireless channel data. The model learns the underlying distribution of measured channel impulse responses and generates high-fidelity synthetic channels that preserve statistical properties. This enables massive data augmentation for training AI-based receiver algorithms, achieving 20% improvement in bit error rate when training data is scarce.
AI Summary
- First application of diffusion models to wireless channel data generation.
- Synthetic channels preserve key statistical properties of real measurements.
- Data augmentation improves receiver AI performance by 20% in low-data regimes.
- Validated across mmWave and sub-6 GHz frequency bands.
Key Findings
- 1Diffusion models capture multi-path channel statistics better than GANs or VAEs.
- 2Generated data is indistinguishable from real data by a trained discriminator.
- 3Augmentation is most beneficial when real measurement data is limited (< 1000 samples).
Industry Implications
Reduces the cost and time of wireless channel measurement campaigns.
Enables robust AI receiver design for 6G frequency bands where data is scarce.
Could accelerate 6G standardization by providing realistic simulation environments.
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