Among the consequences of a crowded electromagnetic spectrum is noise—a lot of it—created mostly by an influx of wireless and other emitters generating a cacophony of signals of varying frequencies and modulation schemes.
Ongoing efforts to share and make more efficient use of available spectrum appear to be falling short as the U.S. military relies more heavily on mobile communications while retaining its ability to counter, for example, electronic warfare tactics.
An emerging approach to the spectrum gap is a signal analysis technique called modulation recognition. (The most common modulation schemes are AM and FM: amplitude and frequency modulation.) The field has generated much research aimed at developing algorithms that can be used to automatically recognize communication signals. If signals can be parsed, operators can use that intelligence to make more efficient use of scarce frequencies.
As spectrum gets noisier, the Defense Advanced Research Projects Agency is encouraging hardware and software engineers to come up with new ways to sort through the racket with new antenna technologies as well as algorithms. With that in mind, DARPA sponsored a recent "Battle of the ModRecs," as in modulation recognition, to test new approaches to navigating the "thicket of waveforms" within the radio frequency spectrum.
Among the goals was demonstrating new modulation recognition techniques for identifying signal origins and types. The DARPA event, which used about 30 modulation schemes, also sought better techniques for sending and receiving information like real-time intelligence data over the least crowded spectral bands.
"Classically, we've described the spectrum strictly by occupancy: There are signals present or not. As the spectrum becomes increasingly filled, we now need more information," explained Paul Tilghman of DARPA's Microsystems Technology Office.
"Modulation recognition is that first step towards getting beyond just describing 'presence' or 'absence' [and] actually describing what is present," Tilghman added.
Another goal was pushing spectrum utilization approaches such as modulation recognition out the laboratory and demonstrating them in real-world scenarios, according to Tom Rondeau, the DARPA program manager.
The DARPA-sponsored "battle" pitted hand-coded expert systems against newer platforms based on emerging machine learning techniques. The hand-coded systems performed better in identifying signal characteristics, but Rondeau predicted the machine learning approach would soon close the gap.
"We now have a better understanding of the state of the art and which directions to explore as we pursue our goal of more effectively managing the spectrum," he added.
The agency argues that modulation recognition is a key step in the effort to achieve "wireless situational awareness" that cloud help squeeze more capacity out of congested electromagnetic spectrum. That awareness could then be used to predict spectrum use and ultimately boost throughput to move data faster via wireless airwaves.