WaSeCom
Wasserstein distributionally robust wireless semantic communication with large AI models.
Robust communication
WaSeCom
Wasserstein distributionally robust wireless semantic communication with large AI models.
- Semantic communication
- Distributional robustness
- Wireless AI
Overview
Reliability across meaning and transmission.
WaSeCom is a model-agnostic framework for improving the reliability and adaptability of wireless semantic communication systems under semantic-level and channel-level uncertainty.
The project combines large AI models with Wasserstein distributionally robust optimization to preserve semantic fidelity under adversarial perturbations, distribution shifts, and challenging wireless channel conditions.
Research focus
Robust by design.
- Robust semantic encoding and decoding under semantic noise, adversarial perturbations, and input distribution shifts.
- Robust channel encoding and decoding under AWGN, Rayleigh fading, interference, and other channel variations.
- Bi-level Wasserstein distributionally robust optimization for semantic and physical layers.
- Model-agnostic integration with transformer-based and multimodal AI architectures.
How it works
A bi-level view of uncertainty.
WaSeCom uses large AI model encoders for compact semantic representations across text, image, audio, and video modalities. A bi-level optimization formulation separates semantic robustness from channel robustness, while dual reformulation and smooth worst-case objectives make training scalable with deep learning models.
Contributions
What the framework brings together.
- A Wasserstein robust framework for wireless semantic communication.
- Robustness guarantees for semantic and channel uncertainty.
- Model-agnostic design across large AI architectures and modalities.
- Empirical validation for image and text semantic communication tasks.
Collaboration
A cross-institution research effort.
This project includes researchers from the University of Sydney, Kyung Hee University, Virginia Tech, Nanyang Technological University, University of Houston, Princeton University, and partner institutions.