DARPA RFMLS exploiting Machine Learning to RF domain for Spectrum sharing and Military IOT systems security

The field of AI has experienced a renaissance fueled by modern data-driven Machine Learning (ML) techniques. In contrast to the expert-engineered processing and decision making of the previous generation of AI, ML-based techniques are able to learn to perform a task from raw sensory input.  ML techniques have advanced the fields of image and speech recognition, and self-driving cars, enabled by sufficient training on digitized writing, spoken words, images, video streams, and other digital content.

 

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.

 

In contrast, the use of AI in the RF domain however has not kept pace with these advancements in ML, and little work has been conducted to explore the intersection of traditional RF signal processing and machine learning.(DARPA) is seeking ways to exploit autonomy and artificial intelligence (AI) to improve spectrum management and address the growing appetite for radio frequency (RF) communication in the military and civil domains.

 

At a time when adversaries have built capabilities to disrupt the RF spectrum, it has become critical to explore how machine learning could be applied to traditional RF signal processing. Through the explosive growth of RF devices and the Internet of Things, the number of connected devices such as phones, sensors, and drones makes it even more important to be able to identify signals intended to hack, spoof, or disrupt RF spectrum usage.

 

The goal of the Radio Frequency Machine Learning Systems (RFMLS) program is to develop the foundations for applying modern ML to the RF Spectrum domain and develop practical applications in emerging spectrum problems, which demand vastly improved discrimination performance over today’s hand-engineered first generation Cognitive RF systems. Ultimately, these innovations will result in a new generation of RF systems that are goal-driven and can learn from data, allowing experts to focus on the overall system capability and not individual subsystem performance.

 

 

One of the applications envisaged by DARPA is IOT security, ML algorithms shall be able to identify the real waveforms with spurious waveforms. 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.

 

“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. One of the requirement of Militaries  is Cognitive Radio (CR),  an adaptive, intelligent radio and network technology that can automatically detect available channels in a wireless spectrum and then change transmission parameters to communicate using them.

 

The [US] Department of Defense (DoD) is the ideal proving ground to try to bring AI to the spectrum, he noted. “We are usually an expeditionary force which means we don’t have an infrastructure to rely on and we don’t have time to conduct site surveys and determine the best place to put a cell tower; we get what we get and have to figure out how to work with it,” Tilghman explained, noting that the diverse range of radios operated by the armed forces further increase the challenge.