Rutgers ECE/WINLAB researchers, Associate Professor Chung-Tse Michael Wu (PI) along with Distinguished Professor Narayan Mandayam and Professor Waheed Bajwa (co-PIs), are the recipients of an award from the National Science Foundation under the Spectrum and Wireless Innovation enabled by Future Technologies (SWIFT) program for the project entitled "Intelligent Spatio-Temporal Metamaterial Massive MIMO Aperture Arrays with Hybrid Learning-based Channel Classifiers for Spectrum-Efficient Secured Wireless Communication."
This three-year project funded at $750,000 aims to harness the spatial dispersion control capability and introduce time modulation for subwavelength metamaterial (MTM) unit cells to create a new intelligent space-time modulated MTM (IST-MTM) antenna aperture, which not only can provide dynamic control of radiation characteristics allowing the optimal spectrum utilization, but also physical-layer (PHY) secure transmission for wireless links enabled by directional modulation (DM). In addition, a new hybrid model-based and learning-based approach (HyPhyLearn) will be incorporated to conduct channel classification even when limited training samples are available. The IST-MTM-based secure communication scheme along with the HyPhyLearn channel classifier is expected to have a profound impact in next-generation wireless networks by providing a highly secured and spectrum-efficient communication scheme, which can be deployed in future 6G networks for smart homes/cities and vehicle-to-vehicle communications.
More information can be found here: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229384
This three-year project funded at $750,000 aims to harness the spatial dispersion control capability and introduce time modulation for subwavelength metamaterial (MTM) unit cells to create a new intelligent space-time modulated MTM (IST-MTM) antenna aperture, which not only can provide dynamic control of radiation characteristics allowing the optimal spectrum utilization, but also physical-layer (PHY) secure transmission for wireless links enabled by directional modulation (DM). In addition, a new hybrid model-based and learning-based approach (HyPhyLearn) will be incorporated to conduct channel classification even when limited training samples are available. The IST-MTM-based secure communication scheme along with the HyPhyLearn channel classifier is expected to have a profound impact in next-generation wireless networks by providing a highly secured and spectrum-efficient communication scheme, which can be deployed in future 6G networks for smart homes/cities and vehicle-to-vehicle communications.
More information can be found here: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229384
Congratulations to Mike, Narayan and Waheed!