Project Details
Description
Future generations of communication systems must support vast numbers of mobile devices that share the spectrum efficiently with different priorities. To meet such a demand, this project goes beyond the traditional techniques of multi-antenna communications and explores a novel paradigm of wireless design by considering the spatial characteristics of signals, in addition to spectrum. This research aims to increase data rates and enable efficient spectrum sharing that could benefit 5G and 6G communication systems, as well as legacy systems. It is increasingly evident that efficient and high-speed communication systems, which are part of the nation's critical infrastructure, help improve society's productivity and maintain the nation's competitive edge. The project will also help train the workforce for the high-tech communication industry.
For achieving the above goal, the key tasks of the project are 1) to estimate and learn the possible spatial signal transmission paths between nodes that form the multipath network, 2) to perform cross-layer design of signal paths selection, coding, broadcast and multiaccess transmissions, 3) and to undertake an experimental verification of the multipath network using software defined radios. Machine learning assisted algorithms will be designed to learn the multipath networks by taking advantage of the repeated patterns. Optimization and coding will be employed to jointly select signal transmission directions and design transmit signals so that the spectrum can be efficiently shared. The expected outcomes are methods for highly effective utilization and sharing of spectrum via a dense reuse of frequency across space.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Status | Active |
---|---|
Effective start/end date | 10/1/18 → 8/31/25 |