NSF/Intel Partnership on Machine Learning for Wireless Networking Systems  (MLWiNS)


CONTACTS
Name Email Phone Room
Monisha  Ghosh mghosh@nsf.gov (703) 292-8746   
Balakrishnan  Prabhakaran bprabhak@nsf.gov 703-292-4847   
Phillip  A. Regalia pregalia@nsf.gov (703) 292-2981   
Anthony  Kuh akuh@nsf.gov (703) 292-2210   
Jenshan  Lin jenlin@nsf.gov 703-292-7950   
Vida  Ilderem vida.ilderem@intel.com (503) 712-5740   
Shilpa  Talwar shilpa.talwar@intel.com (408) 785-6151   
Nageen  Himayat nageen.himayat@intel.com (408) 765-5043   
Jeff  Parkhurst jeff.parkhurst@intel.com (916) 356-2508   


PROGRAM GUIDELINES

Solicitation  19-591

Important Information for Proposers

A revised version of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) (NSF 19-1), is effective for proposals submitted, or due, on or after February 25, 2019. Please be advised that, depending on the specified due date, the guidelines contained in NSF 19-1 may apply to proposals submitted in response to this funding opportunity.


SYNOPSIS

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.


RELATED URLS

What Has Been Funded (Recent Awards Made Through This Program, with Abstracts)

Map of Recent Awards Made Through This Program