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


September 25, 2019

In partnership with Intel, the National Science Foundation’s Directorate for Computer and Information Science and Engineering and Directorate for Engineering announce their continued investment in the development of future wireless systems through the Machine Learning for Wireless Networking Systems (MLWiNS) solicitation. This is the latest in a series of joint efforts between the two partners to support research that accelerates innovation and grows the number of workforce-ready graduates. Over the past five years, NSF and Intel have collectively contributed over $30 million to support pre-competitive science and engineering research.

Future wireless networks will extend beyond current cellular and Wi-Fi systems to support a range of services such as real-time autonomous machines, safety-critical health applications, and augmented and virtual reality, to name a few. It is vital that future systems meet the density, throughput, and latency requirements of such complex applications in an efficient, secure manner. Moreover, wireless deployments that reduce strain on an already over-congested wireless spectrum will become more critical as the demand for capacity and coverage increases.

Machine learning has emerged as a disruptive technique and architectural framework to potentially manage this growing complexity and scale while maintaining quality of service. Through MLWiNS, NSF and Intel aim to accelerate fundamental, broad-based research on wireless-specific machine learning techniques that will enable new wireless architectures and systems for future applications.

With $9 million in funding over the course of three years, the program is anticipated to result in 10-15 awards ranging from $300,000 to $1,500,000 per project for periods of up to three years, pending the availability of funds and quality of proposals received. Proposals must address one or more of the following research vectors:

  • Machine Learning for Wireless Networks: solutions that enable management of large-scale, multi-radio, ultra-dense wireless networks to address the growing complexity and scale of future edge infrastructure and application systems.
  • Machine Learning for Spectrum Management: creation and curation of spectrum data, analysis techniques to enable secure and robust dynamic spectrum access, monitoring and detection of spectrum abuse, and adaptive protocol selection to improve coexistence among both active and passive users of spectrum.
  • Distributed Machine Learning over Wireless Edge Networks: development of distributed machine learning approaches that are adapted for use close to data sources—i.e., approaches that are cognizant of the communication, computation, and architectural constraints of the wireless edge.

NSF and Intel discussed submission requirements, program updates, and specific questions from researchers in a webinar last week. To watch the webinar and learn more, register here.  

 

The U.S. National Science Foundation propels the nation forward by advancing fundamental research in all fields of science and engineering. NSF supports research and people by providing facilities, instruments and funding to support their ingenuity and sustain the U.S. as a global leader in research and innovation. With a fiscal year 2020 budget of $8.3 billion, NSF funds reach all 50 states through grants to nearly 2,000 colleges, universities and institutions. Each year, NSF receives more than 40,000 competitive proposals and makes about 11,000 new awards. Those awards include support for cooperative research with industry, Arctic and Antarctic research and operations, and U.S. participation in international scientific efforts.

mail icon Get News Updates by Email 

Useful NSF Web Sites:
NSF Home Page: https://www.nsf.gov
NSF News: https://www.nsf.gov/news/
For the News Media: https://www.nsf.gov/news/newsroom.jsp
Science and Engineering Statistics: https://www.nsf.gov/statistics/
Awards Searches: https://www.nsf.gov/awardsearch/