Performing signal detection and classification using a trained deep neural network takes a few milliseconds. Compared to iterative and algorithmic signal search, detection, and classification using traditional methodologies, this can represent several orders of magnitude in performance improvement. These gains also translate to reduced power consumption and computational requirements, and the trained models typically provide at least twice the sensitivity of existing approaches.
DeepSig, a US-based startup focused on signal processing and radio systems, has commercialized DL-based RF sensing technology in its OmniSIG Sensor software product, which is compatible with NI and Ettus Research USRPs. Using DL’s automated feature learning, the OmniSIG sensor recognizes new signal types after being trained on just a few seconds' worth of signal capture.
Figure 2. The OmniSIG sensor performs detection and classification of signals within the cellular band, using a general-purpose SDR.
For learned communications systems, including end-to-end learning that facilitates training directly over the physical layer, you can use DeepSig’s OmniPHY software to learn communications systems optimized for difficult channel conditions, hostile spectrum environments, and limited hardware performance. These include non-line-of-sight communications; antijam capabilities; multiuser systems in contested environments; and hardware-distortion-effect mitigation.
One of the advantages of learned communications systems is easy optimization for different missions. While many users care most about throughput and latency, some might prioritize operational link distance, power consumption, or even signature and probability of detection or interception. Moreover, with machine learning, the more you know about the operational environment, the more effective your trained solution can be.
Combining DL-based sensing and active radio waveforms makes possible entirely new classes of adaptive waveforms and EW capable of coping with today’s contested spectrum environments. For DL-based system training, processor performance is key, but once trained, the model can readily be deployed into low-SWaP embedded systems, such as edge sensors and tactical radios.