A Photonics-Inspired Compact Network: Toward Real-Time AI Processing in Communication Systems

• Published in: IEEE journal of selected topics in quantum electronics 2022-07, Vol.28 (4: Mach. Learn. in Photon. Commun. and Meas. Syst.), p.1-17
• Main Authors: Peng, Hsuan-Tung, Lederman, Joshua C., Xu, Lei, de Lima, Thomas Ferreira, Huang, Chaoran, Shastri, Bhavin J., Rosenbluth, David, Prucnal, Paul R.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Classifiers; Communication; Communication systems; Communications systems; Emitters; Fiber nonlinear dispersion compensation; Fingerprint recognition; Fingerprinting; Hardware; Machine learning; Neural networks; Optical fiber networks; Optical fibers; Photonics; Radio frequency; Real time; Real-time systems; Recurrent neural networks; RF fingerprinting; Signal classification; Signal reconstruction; Silicon; silicon photonic neural network; Silicon photonics
• ISSN: 1077-260X; 1558-4542
• DOI: 10.1109/JSTQE.2022.3195824

Machine learning methods are ubiquitous in communication systems and have proven powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation classification, and signal recovery in communication systems. However, the high throughput requirement of a communication link makes AI models difficult to implement in real-time on edge devices. In this work, we address this issue by improving both the algorithm and hardware to target real-time AI processing in communication systems. For algorithm development, we propose the first compact deep network consisting of a silicon photonic recurrent neural network model in combination with a simplified convolutional neural network classifier to identify RF emitters by their random transmissions. Our model achieves 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 (Merchant et al., 2018). Thanks to the large reduction in network size, we emulate the system using a small-scale FPGA board, the PYNQ-Z1, and demonstrate real-time RF fingerprinting with 0.219 ms latency. In addition, for hardware implementation, we further demonstrate a fully-integrated silicon photonic neural network for fiber nonlinearity compensation (Huang et al., 2021), which improves the received signal by 0.60 dB.