A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON
arXiv:2405.00720v13 citationsh-index: 3OFC
Originality Incremental advance
AI Analysis
This work addresses signal equalization for high-speed passive optical networks, representing an incremental improvement with specific gains in performance and efficiency.
The paper tackles the problem of signal degradation in a downstream 100G PAM-4 PON with 28.7 dB path loss by proposing a frequency-calibrated SCINet equalizer, which improves the BER by 88.87% compared to existing methods while reducing complexity by 10.57%.
A frequency-calibrated SCINet (FC-SCINet) equalizer is proposed for down-stream 100G PON with 28.7 dB path loss. At 5 km, FC-SCINet improves the BER by 88.87% compared to FFE and a 3-layer DNN with 10.57% lower complexity.