@inproceedings{7c2b7ffcb52e435f85fddf0e51e287cb,
title = "Low-Complexity FPGA-Accelerated NN-Based Adaptive Equalizer for 100 Gb/s IMDD PON",
abstract = "We demonstrate a low-complexity, field-programable gate array (FPGA)-based adaptive neural network equalizer to mitigate nonlinear impairments caused by semiconductor optical amplifier (SOA) gain saturation in a 100 Gb/s intensity modulation with direct detection (IMDD) passive optical network (PON). The proposed equalizer employs a 32-tap feedforward neural network (FFNN) for multi-symbol detection. This approach incorporates both offline training and adaptive learning techniques to ensure real-time adaptability. To enhance FPGA efficiency, the model is quantized to an 8-bit fixed-point format, and the FFNN core is parallelized to achieve a 100 Gb/s throughput. Experimental results show a dynamic range of 27.8 dB and a sensitivity of -22.8 dBm. This approach improves real-time digital signal processing and establishes a foundation for future machine learning-based solutions in next-generation PON systems, addressing key performance challenges.",
keywords = "equalizer, FPGA, neural network, passive optical network",
author = "Ehsan Roshanshomal and Murphy, \{Stephen L.\} and Ayat, \{S. Omid\} and Fariba Jamali and Townsend, \{Paul D.\} and Cleitus Antony",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2nd IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025 ; Conference date: 26-05-2025 Through 29-05-2025",
year = "2025",
doi = "10.1109/ICMLCN64995.2025.11140557",
language = "English",
series = "2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 IEEE International Conference on Machine Learning for Communication and Networking, ICMLCN 2025",
address = "United States",
}