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Low Complexity Neural Network Equalization Based on Multi-Symbol Output Technique for 200+ Gbps IM/DD Short Reach Optical System

Nowadays, Neural network (NN) has been proved to be an effective solution for nonlinear equalization in short reach optical systems. However, recent research has mainly focused on implementing more powerful NNs for equalization, while ignoring their adaptability to equalization tasks. In this paper, we propose Multi-Symbol Output (MSO)-Neural Networks (NN) for nonlinear equalization in high-speed short reach optical interconnects. The results show that the proposed MSO design works well on Deep Neural Networks (DNN), Long Short-Term Memory neural networks (LSTM) and Gate Recurrent Unit (GRU), which are the recent NN-based equalization structures. By increasing output symbols of the NNs, the number of slide windows in equalization can be sharply reduced, and so the complexity is reduced. By the same time, more information is brought to the MSO-NNs in back-propagations, therefore performance gain achieved. A 212-Gb/s 1-km Pulse Amplitude Modulation (PAM)-4 optical link is experimentally demonstrated as the target system, and the proposed MSO-NN equalizers are used to compare with traditional equalization algorithms including Volterra Nonlinear Equalizer (VNE) and single-symbol output NNs. Experimental results show that MSO design could help reduce the complexity of NN required for nonlinear equalization in the target system by around 2/3, and the proposed MSO-LSTM performs much better than VNE and 1 dB better than SSO-LSTM at the same time. Based on the proposed MSO-LSTM, transmission with BER under HD-FEC over 1 km NZDSF is achieved with a ROP at -2 dBm. Our work is well expandable and the proposed MSO design can be extended to other NN-based equalizers, which can help reduce complexity and learn more info from the training data and gain performance. The proposed MSO-NNs further enhance the performance and reduces the complexity of NN equalizers, provides assurance for future real-time high-speed short-range optical systems, and brings new ideas to NN-based equalizer design.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.40 No.9 May 1 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2890-2900

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

NEURAL NETWORK
NONLINEAR EQUALIZATION
IM/DD SYSTEM
MULTI-SYMBOL OUTPUT

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3146863

Pernyataan Tanggungjawab

Bohan Sang, Wen Zhou, Yuxuan Tan, Miao Kong, Chen Wang, Mingxu Wang, Li Zhao, Jiao Zhang, Jianjun Yu

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