A Photonic-Circuits-Inspired Compact Network: Toward Real-Time Wireless Signal Classification at the Edge

TL;DR

This paper introduces a compact, photonic-inspired neural network for real-time wireless signal classification at the edge, achieving high accuracy with significantly fewer parameters and enabling low-latency processing on FPGA hardware.

Contribution

A novel photonic-hardware-inspired recurrent neural network combined with a simplified classifier for efficient RF emitter identification at the edge.

Findings

Achieved 96.32% accuracy on RF emitter classification.

Reduced training parameters by 50 times compared to state-of-the-art CNN.

Enabled real-time classification with 0.219 ms latency on FPGA.

Abstract

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.