Deep learning-based image-like channelization for broadband receiver

Yu Huang; Jiejun Zhang; Jianping Yao

doi:10.1117/12.2656199

9 January 2023 Deep learning-based image-like channelization for broadband receiver

Yu Huang, Jiejun Zhang, Jianping Yao

Proceedings Volume 12507, Advanced Optical Manufacturing Technologies and Applications 2022; and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2022); 125071X (2023) https://doi.org/10.1117/12.2656199
Event: 2nd International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2022), 2022, Changchun, China

Abstract

We propose a novel image-like channelization method that utilizes a convolutional recurrent neural network (CRNN) for channel synthesis to reduce the bandwidth requirements of the electrical hardware. In this study, the spectrum of a 30-GBaud QPSK signal is spectrally sliced and received by four low-speed coherent receivers based on a conventional coherent optical communication system. After the recovery of the trained CRNN, the average error vector magnitude (EVM) of the 30-GBaud baseband signal is improved from over 60% by uncorrected channel synthesis to around 15%.

Citation Download Citation

Yu Huang, Jiejun Zhang, and Jianping Yao "Deep learning-based image-like channelization for broadband receiver", Proc. SPIE 12507, Advanced Optical Manufacturing Technologies and Applications 2022; and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2022), 125071X (9 January 2023); https://doi.org/10.1117/12.2656199

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