  Current radio frequency (RF) sensors at the Edge lack the computational
resources to support practical, in-situ training for intelligent spectrum
monitoring, and sensor data classification in general. We propose a solution
via Deep Delay Loop Reservoir Computing (DLR), a processing architecture that
supports general machine learning algorithms on compact mobile devices by
leveraging delay-loop reservoir computing in combination with innovative
electrooptical hardware. With both digital and photonic realizations of our
design of the loops, DLR delivers reductions in form factor, hardware
complexity and latency, compared to the State-of-the-Art (SoA). The main impact
of the reservoir is to project the input data into a higher dimensional space
of reservoir state vectors in order to linearly separate the input classes.
Once the classes are well separated, traditionally complex, power-hungry
classification models are no longer needed for the learning process. Yet, even
with simple classifiers based on Ridge regression (RR), the complexity grows at
least quadratically with the input size. Hence, the hardware reduction required
for training on compact devices is in contradiction with the large dimension of
state vectors. DLR employs a RR-based classifier to exceed the SoA accuracy,
while further reducing power consumption by leveraging the architecture of
parallel (split) loops. We present DLR architectures composed of multiple
smaller loops whose state vectors are linearly combined to create a lower
dimensional input into Ridge regression. We demonstrate the advantages of using
DLR for two distinct applications: RF Specific Emitter Identification (SEI) for
IoT authentication, and wireless protocol recognition for IoT situational
awareness.
