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We designed and implemented a deep learning based RF signal classifier on theField Programmable Gate Array (FPGA) of an embedded software-defined radioplatform, DeepRadio, that classifies the signals received through the RF frontend to different modulation types in real time and with low power. Thisclassifier implementation successfully captures complex characteristics ofwireless signals to serve critical applications in wireless security andcommunications systems such as identifying spoofing signals in signalauthentication systems, detecting target emitters and jammers in electronicwarfare (EW) applications, discriminating primary and secondary users incognitive radio networks, interference hunting, and adaptive modulation.Empowered by low-power and low-latency embedded computing, the deep neuralnetwork runs directly on the FPGA fabric of DeepRadio, while maintainingclassifier accuracy close to the software performance. We evaluated theperformance when another SDR (USRP) transmits signals with different modulationtypes at different power levels and DeepRadio receives the signals andclassifies them in real time on its FPGA. A smartphone with a mobile app isconnected to DeepRadio to initiate the experiment and visualize theclassification results. With real radio transmissions over the air, we showthat the classifier implemented on DeepRadio achieves high accuracy with lowlatency (microsecond per sample) and low energy consumption (microJoule persample), and this performance is not matched by other embedded platforms suchas embedded graphics processing unit (GPU).

Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification