Data science institute develops method to allow mobile users to tap into RF-spectrum.
The Data Science Institute and the Electrical Engineering Department at Columbia University received a National Science Foundation grant ($649,963) to develop energy-efficient sensors that will allow mobile and wireless-device users to tap into available, unused channels in the radio-frequency spectrum. The sensors will enable future communication systems to flexibly share the spectrum.
Wireless communications and mobile applications have placed an enormous strain on the electromagnetic spectrum – a finite and limited resource. Large portions of the spectrum, moreover, are already allocated to primary users such as fire departments, the coast guard and firs- responders, who use their channels only in exceptional circumstances. And that’s why there’s a need for sensors and computational techniques that can detect and use the available spectrum when it’s not engaged by primary users.
“At some point in the future as we keep using more and more mobile devices, the spectrum will run out of space,” said John Wright, a DSI affiliate and electrical engineering professor who is the principal investigator on the project. “We’ll use all the data-science tools we possess – machine learning, neural networks, algorithms and advanced computation techniques, in conjunction with new hardware devices – to sense pieces of the RF spectrum as they become available.”
Wright said that Peter Kinget, an electrical engineering professor at Columbia who specializes in analog and radio-frequency (RF) integrated circuits, will design circuits that can create “snapshots” of a large portion of the spectrum. Wright will then use a few of the snapshots to design algorithms to reconstruct the spectrum and help design a more energy-efficient sensor.
What’s novel about this project, said Wright, is that it mixes the latest computational methods with novel hardware design. On the data-science side, he will lead a team to develop computational techniques – algorithms and machine-learning methods to model and predict the available areas of the spectrum. Kinget’s team, on the other hand, will design new hardware – the circuits to sense the available channels in the spectrum.
“This project builds upon our ongoing fruitful collaboration with John’s team,” said Kinget. “In the past couple of years we have demonstrated several RF spectral sensors that generally used off-the-shelf signal-processing approaches with our custom hardware and have demonstrated significant speed and energy benefits. It will be exciting to see how much more progress we can make using new algorithms built on the latest insights in signal processing.”
Wright agrees that the multidisciplinary nature of the project will help it succeed.
“What’s exciting about the research is that the algorithms and computational tools my team is developing can enhance the circuits that Peter’s team is designing,” he said. “And we hope this project will lead to energy-efficient ways to detect and use the RF spectrum, so it continues to be available to the escalating number of wireless-communication users and mobile applications.” (content credit: November 8, 2017, Robert, Florida, https://datascience.columbia.edu)
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