We demonstrate a first example for employing deep learning in predicting frame errors for a Collaborative Intelligent Radio Network (CIRN) using a dataset collected during participation in the final scrimmages of the DARPA SC2 challenge. Four scenarios are considered based on randomizing or fixing the strategy for bandwidth and channel allocation, and either training and testing with different links or using a pilot phase for each link to train the deep neural network. Interestingly, we unveil the efficacy of randomization in improving detection accuracy and the generalization capability of certain deep neural network architectures with Bootstrap Aggregating (Bagging).

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?"

DARPA today announced the newest of its Grand Challenges, one designed to ensure that the exponentially growing number of military and civilian wireless devices will have full access to the increasingly crowded electromagnetic spectrum. The agency's Spectrum Collaboration Challenge (SC2) will reward teams for developing smart systems that collaboratively, rather than competitively, adapt in real time to today's fast-changing, congested spectrum environment--redefining the conventional spectrum management roles of humans and machines in order to maximize the flow of radio frequency (RF) signals. DARPA officials unveiled the new Challenge before some 8000 engineers and communications professionals gathered in Las Vegas at the International Wireless Communications Expo (IWCE). The primary goal of SC2 is to imbue radios with advanced machine-learning capabilities so they can collectively develop strategies that optimize use of the wireless spectrum in ways not possible with today's intrinsically inefficient approach of pre-allocating exclusive access to designated frequencies. The challenge is expected to both take advantage of recent significant progress in the fields of artificial intelligence and machine learning and also spur new developments in those research domains, with potential applications in other fields where collaborative decision-making is critical.

DARPA has announced plans to hold a competition pitting electromagnetic spectrum receivers (think: TVs, wifi-enabled computers, radios). As the amount of mobile data traffic is growing at an exponentially rapid rate, DARPA believes we need to start refining the way we handle the crowded spectrum. The Spectrum Collaboration Challenge (SC2) will see research teams collaborating to create smart systems to share wavelengths using algorithms and artificial intelligence. Global mobile data traffic grew by 74 percent in 2015, and more than half a billion devices and connections were added to the overwhelmingly clogged spectrum, according to recent Cisco reports. Analysts expect that the monthly global mobile traffic rates will reach 30.6 exabytes by 2020 (rates are at 3.7 exabytes per month as of 2015).