Mr. Huan Chang Profile

Huan Chang

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Proceedings Article | 22 January 2025 Paper

Optical performance monitoring of mode division multiplexing signal based on multitask convolutional neural network

Ting He, Huan Chang, Ran Gao, Fei Wang, Dong Guo

Proceedings Volume 13520, 135200M (2025) https://doi.org/10.1117/12.3057047

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Mode Division Multiplexing (MDM) optical fiber communication technology enhances bandwidth utilization and communication capacity to address the increasing demand for high-capacity data transmission in the era of rapid network traffic growth. For MDM optical fiber communication systems affected by various impairments, Optical Performance Monitoring (OPM) can effectively characterize the impairments in received optical signals, directly reflecting the transmission signal state and channel conditions, and help ensure efficient and stable network operation. In this paper, we propose a Multitask Convolutional Neural Network (MT-CNN) aided OPM method for MDM signals. The proposed MT- CNN combines convolutional neural networks and a multitask learning framework to extract the transmission link information and predict the Optical Signal-to-Noise Ratio (OSNR) of few-mode channels synchronously. Simulation results show that the proposed MT-CNN-based OPM scheme can achieve accurate OSNR performance evaluation for MDM fiber transmission signals.

Proceedings Article | 22 January 2025 Paper

An end-to-end autoencoder-based probabilistic shaping method for optical communication

Ziqiang Teng, Dong Guo, Ran Gao, Leyi Kong, Wentao Han, Huan Chang, Zhipei Li, Xiangjun Xin

Proceedings Volume 13520, 135200E (2025) https://doi.org/10.1117/12.3056362

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The advent of services such as big data and cloud computing has driven a continuous increase in the required transmission rate and capacity of optical fiber communication systems, which now support over 90% of global data traffic. Probability Shaping (PS), which adjusts the probability distribution of constellation points, enhances both data transmission rates and the resilience of signals to channel nonlinearity. However, traditional PS methods, such as those using Constant Composition Distribution Matching (CCDM), cannot dynamically adjust the probability distribution of constellation points based on channel conditions. To address this limitation, we propose an end-to-end Autoencoder (AE)-based PS technique for optical communication. Simulation results show that compared to traditional 16/64/256QAM, this scheme achieves a maximum improvement of 0.356 bit/symbol in mutual information over the AWGN channel. In the optical fiber channel, under varying signal power and fiber length conditions, the mutual information is improved in different degrees and the influence of channel nonlinearity on the signal is reduced. Innovatively, residual connections are added to the decoder to enhance the model’s generalization performance, enabling a deeper network structure and improving its ability to handle complex optical fiber channels.

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