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Current radio frequency (RF) sensors at the Edge lack the computationalresources to support practical, in-situ training for intelligent spectrummonitoring, and sensor data classification in general. We propose a solutionvia Deep Delay Loop Reservoir Computing (DLR), a processing architecture thatsupports general machine learning algorithms on compact mobile devices byleveraging delay-loop reservoir computing in combination with innovativeelectrooptical hardware. With both digital and photonic realizations of ourdesign of the loops, DLR delivers reductions in form factor, hardwarecomplexity and latency, compared to the State-of-the-Art (SoA). The main impactof the reservoir is to project the input data into a higher dimensional spaceof reservoir state vectors in order to linearly separate the input classes.Once the classes are well separated, traditionally complex, power-hungryclassification models are no longer needed for the learning process. Yet, evenwith simple classifiers based on Ridge regression (RR), the complexity grows atleast quadratically with the input size. Hence, the hardware reduction requiredfor training on compact devices is in contradiction with the large dimension ofstate vectors. DLR employs a RR-based classifier to exceed the SoA accuracy,while further reducing power consumption by leveraging the architecture ofparallel (split) loops. We present DLR architectures composed of multiplesmaller loops whose state vectors are linearly combined to create a lowerdimensional input into Ridge regression. We demonstrate the advantages of usingDLR for two distinct applications: RF Specific Emitter Identification (SEI) forIoT authentication, and wireless protocol recognition for IoT situationalawareness.

Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors