This program seeks to accelerate fundamental, broad-based research on wireless-specific machine learning (ML) techniques, towards a new wireless system and architecture design, which can dynamically access shared spectrum, efficiently operate with limited radio and network resources, and scale to address
the diverse and stringent quality-of-service requirements of future wireless applications.
In parallel, this program also targets research on reliable distributed ML by addressing the challenge of computation over wireless edge networks to enable ML for wireless and future applications.
Model-based approaches for designing the wireless network stack have proven quite efficient in delivering the networks in wide use today; research enabled by this program is expected to identify realistic problems that can be best solved by ML and to address fundamental questions about expected improvements from using ML over model-based methods.
Proposals may address one or more Research Vectors (RVs):
ML for Wireless Networks; ML for Spectrum Management; and Distributed ML over Wireless Edge Networks.
It is anticipated that 10 to 15 awards will be made, with an award size of $300,000-$1,500,000, for periods of up to 3 years.
The budget should be commensurate with the complexity of the proposed research.
Projects will be funded across this range.