The Defense Advanced Research Projects Agencies is looking to apply the same kind of machine learning to the radio spectrum as is used by advanced systems for applications ranging from voice recognition to management of internet-of-things devices to autonomous vehicles. DARPA has issued a broad agency announcement for a new Radio Frequency Machine Learning Systems (RFMLS) program that will address the need for enhanced situational awareness regarding the ever-changing composition of RF signals in the IoT and spectrum sharing. Machine learning is widely used to manage data and images, but the similar work in the radio spectrum offers unique challenges, making a more compelling case for developing a native approach. "What I am imagining is the ability of an RF machine learning system to see and understand the composition of the radio frequency spectrum – the kinds of signals occupying it, differentiating those that are'important' from the background, and identifying those that don't follow the rules," said DARPA's Microsystems Technology Office Program Manager Paul Tilghman. An RFMLS would be able to discern subtle differences in the RF signals among identical, mass-manufactured IoT devices and identify signals intended to spoof or hack into these devices.

Launched in 2016, SC2's goal is to create a collaborative machine-learning competition to address radio frequency (RF) spectrum challenges. DARPA experts created SC2 to help users of the existing radio spectrum overcome the problem of clogged spectrum. Demand for radio spectrum has grown steadily over the past century, and in the past several years has increased at a rate of 50 per-cent per year. SC2 wants to move away from traditional ways of communicating via one frequency. As Paul Tilghman explained during his keynote speech at NIWeek, one of the biggest obstacles in spectrum management is that "frequency isolation completely dominates our spectrum landscape."

Modern radar and communications systems can subtly and quickly change their character, making them harder for U.S. aircraft and other platforms to jam or spoof. That reality is prompting DARPA to lead industry teams to apply artificial intelligence to electronic warfare. The difference between today's tech and that of the 1970s lies in the adoption of readily available digital processing. Such processing effectively allows operators to change aspects of the waveforms that radar and communications systems use. "The problem now is that if we continue to rely on that [old] approach, the radar waveforms we're expecting could be rapidly changed," Tilghman says.

Modern radar and communications systems can subtly and quickly change their character, making them harder for U.S. aircraft and other platforms to jam or spoof. That reality is prompting DARPA to lead industry teams to apply artificial intelligence to electronic warfare. The U.S. military developed its current approach to EW in the 1960s and 70s when it studied enemy systems to identify their vulnerabilities. It then came up with countermeasures to disrupt them, which went into a sort of tactical EW "playbook." "Cognitive EW is being developed to deal with the unexpected."

The Pentagon's research agency has a new challenge for scientists: make wireless radios with artificial intelligence that can figure out the most effective, efficient way to use the radio frequency spectrum, and win a pile of cash. Winners of the Defense Advanced Research Projects Agency's (DARPA) Spectrum Collaboration Challenge (SC2) could take home up to 3.5 million, but to do that, teams will have to demonstrate new technologies that represent a "paradigm shift" with both military and commercial applications, said Paul Tilghman, a DARPA program manager who is leading the challenge. "The real crux of the problem is -- when you look at users of the spectrum, whether they are commercial users of the spectrum, whether they're consumers or they're the military -- the thing that is ubiquitously true is we all are placing more and more and more demand on the spectrum, and all of that demand is really adding up and going to stress the way that we actually manage the spectrum," he said. "Where do we put our communications systems? Where do we put our radars? Where do we put our [electronic warfare] systems?"