Industry-Sponsored Research Week

Intel, NSF fund research into use of AI for future wireless networks

By David Schwartz
Published: June 30th, 2020

A new research program funded by Intel and the National Science Foundation are aimed at improving future wireless networks by applying AI and machine learning technologies. Most of the 15 projects that have just been funded under of the Machine Learning for Wireless Networking Systems (MLWiNS) program focus on the use of deep learning or neural networks in a wireless environment.

The award sizes range from $300,000 to $1.5 million for periods up to three years. MLWiNS is aimed at accelerating “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.” The program also seeks to advance “reliable distributed ML by addressing the challenge of computation over wireless edge networks to enable ML for wireless and future applications.”

The program description for the funding says researchers are “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.”

“5G and Beyond networks need to support throughput, density and latency requirements that are orders of magnitudes higher than what current wireless networks can support, and they also need to be secure and energy-efficient,” said Margaret Martonosi, assistant director for computer and information science and engineering at NSF. “The MLWiNS program was designed to stimulate novel machine learning research that can help meet these requirements.”

The funded projects include:

  • At Rice University, researchers will work to train large-scale centralized neural networks by separating them into a set of independent sub-networks that can be trained on different devices at the edge. This can reduce training time and complexity, while limiting the impact on model accuracy.
  • Research teams from the Massachusetts Institute of Technology and Virginia Polytechnic Institute and State University will “explore the use of deep neural networks to address physical layer problems of a wireless network” and work to “develop new algorithms that can better address non-linear distortions and relax simplifying assumptions on the noise and impairments encountered in wireless networks.”
  • University of California-Irvine researchers will work to develop ML methodologies to provide “reliable distributed computing in drone-infrastructure systems” with a “layer of intelligence located in individual drones” plus methods to compress data streams to be transferred for remote analysis.
  • University of Notre Dame researcher will address the question of “sensor quality vs. quantity” when it comes to spectrum measurement and sensing.

Source: RCR Wireless News

Posted under: University-Industry Engagement Week

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