- of dealing with non-independent and identically distributed data, and heterogeneous resources at the wireless edge, and minimizing upload bandwidth costs from users, while emphasizing issues of privacy and security when learning from distributed data.
- Distributed training across multiple edge devices : Rice University researchers will work to train large-scale centralized neural networks by separating them into a set of independent sub-networks that can be trained on different devices at the edge. This can reduce training time and complexity, while limiting the impact on model accuracy.
- Leveraging information theory and machine learning to improve wireless network performance : Research teams from the Massachusetts Institute of Technology and Virginia Polytechnic Institute and State University will collaborate to explore the use of deep neural networks to address physical layer problems of a wireless network. They will exploit information theoretic tools in order to develop new algorithms that can better address non-linear distortions and relax simplifying assumptions on the noise and impairments encountered in wireless networks.
- Deep learning from radio frequency signatures : Researchers at Oregon State University will investigate cross-layer techniques that leverage the combined capabilities of transceiver hardware, wireless radio frequency (RF) domain knowledge and deep learning to enable efficient wireless device classification. Specifically, the focus will be on exploiting RF signal knowledge and transceiver hardware impairments to develop efficient deep learning-based device classification techniques that are scalable with the massive and diverse numbers of emerging wireless devices, robust against device signature cloning and replication, and agnostic to environment and system distortions.
See " Intel and National Science Foundation Announce Future Wireless Systems Research Award Recipients " for a full list of the award winners and project descriptions.