The radio frequency spectrum is becoming increasingly crowded and a new DARPA program will examine how leading-edge machine learning can help understand all the signals in the crowd
The current wave of artificial intelligence, driven by machine learning (ML) techniques, is all the rage, and for good reason. With sufficient training on digitized writing, spoken words, images, video streams, and other digital content, ML has become the basis of voice recognition, self-driving cars, and other previously only-imagined capabilities. As billions of phones, appliances, drones, traffic lights, security systems, environmental sensors, and other radio-connected devices sum into a rapidly growing Internet of Things (IoT), there now is a need to apply ML to the invisible realm of radio frequency (RF) signals, according to program manager Paul Tilghman of DARPA’s Microsystems Technology Office. To further that cause, DARPA today announced its new Radio Frequency Machine Learning Systems (RFMLS) program.
“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 Tilghman. He would want that same system to be able to discern subtle but inevitable differences in the RF signals from what otherwise are identical, mass-manufactured IoT devices and to distinguish these from signals intended to spoof or hack into these devices. “We want to be able to understand and trust what is happening in the Internet of Things and to stand up an RF forensics capability to identify unique and peculiar signals amongst the proverbial cocktail party of signals out there,” said Tilghman.
The same situational awareness regarding the ever-changing composition of RF signals in any given space should also support a wireless communications management paradigm known as spectrum sharing. That’s a paradigm of shared spectrum use rather than the current practice of exclusive allocations governed by license agreements for specific frequencies. Tilghman is hoping to develop technologies to understand the current state of the spectrum for improved and extensive spectrum sharing—which can greatly expand the wireless communications capacity of the electromagnetic spectrum—both in the RFMLS program as well as in another major DARPA effort known as the Spectrum Collaboration Challenge.
AI’s first and ongoing wave consists of expert systems that rigidly codify human expertise and decision-making in predictable, rule-driven domains, such as simple game playing, tax preparation, and industrial process control. Such expert systems also have been deployed in RF contexts where, for example, engineers have been able to specify in computer code the rigid rules used by radios to switch to unused frequencies when they encounter interference. While effective, these systems have little understanding of what’s actually happening in the spectrum. RF applications of the second and emerging machine-learning wave of AI should yield far more agile and versatile capabilities: an RFML system, with a sufficiently rich training set of RF data, should be able to identify an enormous range of both known and previously unseen RF waveforms.
The RFMLS program features four technical components that would integrate into future RFML systems:
Feature Learning: From data sets of RF signals, RFML systems will need to learn the characteristics used to identify and characterize signals in various civilian and military settings.
Attention and Saliency: Just as people can quickly direct their attention for a needed goal—finding ice cream in a huge supermarket, for example—amidst the morass of sensory input coming in at every moment, an RFML system will need to include algorithms for directing its artificial attention to what is potentially important in the RF spectrum it is operating in. Researchers who win contracts to work on the RFMLS program will need to devise an equivalent within the RF domain of our own so-called salience detection, that is, the ability to identify and recognize important visual and auditory stimuli. The presence of a communications signal in a frequency band usually devoted to radar signals would be an example of a signal-of-interest that an RFMLS’s salience-detection capability would have to notice.
Autonomous RF Sensor Configuration: Our eyes automatically adjust to changing light levels and they move and focus to keep the most important aspects of a dynamic visual scene in the most sensitive portions of the retina. The RFML systems that DARPA envisions would have an equivalent ability to automatically tune their receptivity to signals and signal features the systems deem to be most effective at accomplishing the task at hand.
Waveform Synthesis: A full RFML system also should be able to digitally synthesize virtually any possible waveform, much as human beings can pronounce any new word or add inflections or pauses to infuse gravitas or nuances of meaning into what they saying. This capability to create new waveforms tailored to the specific RF devices they emanate from should give other sophisticated radios the improved ability to identify friendly systems.
“If we get this right, we will have RF systems with the ability to discern and characterize signals in the ever-more-crowded spectrum. And that will give emerging automated systems, and the military commanders that rely on them, much needed information to understand the landscape of the wireless domain,” said Tilghman. “I hope our new RFMLS program will forge the technical foundations for a new domain and community of AI research.”