  Machine learning (ML) methods are ubiquitous in wireless communication
systems and have proven powerful for applications including radio-frequency
(RF) fingerprinting, automatic modulation classification, and cognitive radio.
However, the large size of ML models can make them difficult to implement on
edge devices for latency-sensitive downstream tasks. In wireless communication
systems, ML data processing at a sub-millisecond scale will enable real-time
network monitoring to improve security and prevent infiltration. In addition,
compact and integratable hardware platforms which can implement ML models at
the chip scale will find much broader application to wireless communication
networks. Toward real-time wireless signal classification at the edge, we
propose a novel compact deep network that consists of a
photonic-hardware-inspired recurrent neural network model in combination with a
simplified convolutional classifier, and we demonstrate its application to the
identification of RF emitters by their random transmissions. With the proposed
model, we achieve 96.32% classification accuracy over a set of 30 identical
ZigBee devices when using 50 times fewer training parameters than an existing
state-of-the-art CNN classifier. Thanks to the large reduction in network size,
we demonstrate real-time RF fingerprinting with 0.219 ms latency using a
small-scale FPGA board, the PYNQ-Z1.
