Low-Complexity FPGA-Accelerated NN-Based Adaptive Equalizer for 100 Gb/s IMDD PON

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.