Ongoing wireless revolution is fueling a voracious demand for access to the radio frequency (RF) spectrum around the world. In the civilian sector, consumer devices from smartphones to wearable fitness recorders to smart kitchen appliances are competing for bandwidth. Around 50 billion wireless devices are projected to be vying for access to mobile communications networks within the next few years and by 2030, the demand for wireless access could be 250 times what it is today. However, as the use of wireless technology proliferates, radios and communication devices often interfere with and disrupt other wireless devices.
Military spectrum requirements are also increasing exponentially as military operations increasingly rely on access to the wireless spectrum in order to assess the tactical environment and coordinate and execute their critical missions. The demand for more and timely information at every echelon is driving an increase in DoD’s need for spectrum.“Increasingly lower echelons, including individual soldiers, require situational awareness information resulting in more spectrum-enabled network links.” Managing this increasing demand, while combating what appears to be a looming scarcity of RF spectrum is a serious problem for our nation, both militarily and economically, says DARPA.
However, spectrum is a finite resource and additionally DOD has to free up 500 MHz of the spectrum it has for commercial use by 2020 leading to scarcity of spectrum for DOD use. Therefore Spectrum congestion is becoming a growing problem, DARPA officials explain. It increasingly limits operational capabilities due to the increasing deployment and bandwidth of wireless communications, the use of network-centric and unmanned systems, and the need for increased flexibility in radar and communications spectrum to improve performance and overcome sophisticated countermeasures.
Currently the spectrum is managed by nearly a century old technique, by isolating wireless systems by dividing the spectrum into exclusively licensed bands, which are allocated over large, geographically defined regions. This approach rations access to the spectrum in exchange for the guarantee of interference-free communication. However, allocation is human-driven and not adaptive to the dynamics of supply and demand. At any given time, many allocated bands are unused by licensees while other bands are overwhelmed, thus squandering the spectrum’s enormous capacity and unnecessarily creating conditions of scarcity.
The current situation also poses potential security risks for the military, creating the impression of reliable and unfettered access to the spectrum while in actuality creating a well-defined target for adversaries that may wish to disrupt wireless operations. First responder radios need to be able to communicate reliably in such congested and contested environments and to share radio spectrum without direct coordination or spectrum preplanning.
In March 2016, DARPA launched the Spectrum Collaboration Challenge (SC2), an initiative designed to ensure that the exponentially growing number of military and civilian wireless devices will have full access to the increasingly crowded electromagnetic spectrum. These networks will be capable of intelligently optimizing the spectrum by collaborating with, and learning from, the other systems that occupy the spectrum with them. SC2 competitors will reimagine spectrum access strategies and develop a new wireless paradigm in which radio networks, will autonomously collaborate and reason about how to share the RF spectrum, avoid interference, and jointly exploit opportunities to achieve the most efficient use of the available spectrum.
DARPA announced in Oct 2019 that GatorWings, a team of undergraduate students, Ph.D. candidates, and professors from the University of Florida are the winners of the Spectrum Collaboration Challenge (SC2) – a three-year competition to unlock the true potential of the radio frequency (RF) spectrum with artificial intelligence (AI). GatorWings’ autonomous radio was able to navigate the various wireless obstacles developed for SC2 to thoroughly stress each team’s AI-enabled radios. GatorWings’ unique approach to the SC2 challenge helped it eke out the competition. Using an AI engine that is one-step beyond basic rule-based systems, GatorWings applied foundational reinforcement learning AI techniques to optimize each “pocket” of available spectrum.
While GatorWings took home the top spot, the second and third place finishers were MarmotE and Zylinium, respectively. MarmotE, a team of current and former Vanderbilt researchers, took home the $1 million second place prize, while the third place prize of $750,000 went to Zylinium, a three-person start-up with expertise in software-defined radios (SDRs) and AI. Andersons, a two-person team of hobbyists and SDR enthusiasts that also successfully competed in DARPA’s 2014 Spectrum Challenge, and Erebus, a three-person company created specifically to tackle SC2, rounded out the top five.
“It was truly a battle right until the end, with GatorWings beating out MarmotE by just one point. Each team took a slightly different approach to the final scenarios – some used AI to navigate the wireless spectrum like a driverless car, while others used machine learning to promote competing or collaborating solutions. In the end, the three highest ranked teams were able to maximize their use of the spectrum by skillfully collaborating with their competitors’ radios while successfully completing as many data transfers as possible,” said Tilghman.
Spectrum Collaboration Challenge administrator Paul Tilghman said: “SC2 sets out to bring the software defined radio and artificial intelligence communities together to fundamentally rethink 100 years of spectrum practice, and tackle the original and enduring spectrum grand challenge: efficient coexistence of all wireless communications.
DARPA’s grand challenges are actually a really good exploratory measure, when you have a very tough problem, but one that’s very tangible, and also one where there’s many possible different types of solutions to that problem. And what you’re really searching for is a way to sift through a large number of potential solutions and neck it down to the one or two or three that are really viable. And again here we find ourselves in the Spectrum Collaboration Challenge with a large number of possible solutions for how you bring intelligence into the radio, and we’re really hoping that by using a competition, that we’re able to determine really which strategies, what kinds of artificial intelligence, are best at optimizing the spectrum under every circumstance.
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