6.4Tb/s SSB WDM Transmission Over 320km SSMF With Linear Network-Assisted LSTM

Fang, Xiansong; Chen, Xinyu; Yang, Fan; Zhang, Lei; Zhang, Fan

doi:10.1109/lpt.2021.3125331

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…A value closer to 1 indicates better clustering performance, while a value closer to -1 indicates worse performance. The silhouette coefficient s(i) for a particular sample is calculated [13].…”

Section: Parameter Calculationmentioning

confidence: 99%

Research on short-term power load forecasting of smart grid based on residual DBSCAN-CNN-LSTM model

li,

Hao,

huang

et al. 2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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Under the "double carbon" goal, China's power system will present the characteristics of "double height" and the balance of supply and demand in power system will face new challenges. A more accurate power load forecasting method is urgently needed. Therefore, a method to visualize the load data is presented and the long short-term memory (LSTM) artificial neural network is used for sho1t-term power load forecasting. Firstly, preprocesses load data using the DBSCAN clustering algo1ithm. Secondly, the graphical time series load data is extracted by convolutional neural network (CNN) and used as the input of LSTM for prediction. Finally, the predicted picture is transformed into time series curve to obtain the final short-term load forecasting results. This method not only enriches the expression of load data, but also highlights the advantages of LSTM method in image analysis and processing. The experimental results show that this method improves the accuracy of short-term load forecasting to a certain extent and meets the needs of practical engineering.

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Section: Parameter Calculationmentioning

confidence: 99%

Research on short-term power load forecasting of smart grid based on residual DBSCAN-CNN-LSTM model

li,

Hao,

huang

et al. 2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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“…In this section, we will introduce the applications of ML techniques in SCD systems including nonlinearity compensation [86,87], IQ imbalance correction [88], PR in SSB [89][90][91], optical field recovery in PR receiver [83,84] and CADD schemes [82,92], and polarization tracking and demultiplexing in JSFR schemes [85]. In addition, the transfer learning [93][94][95] technique has been employed to realize fast remodeling in SSB system, which could be scalable to other DD systems.…”

Section: Techniques In Scd Systemmentioning

confidence: 99%

“…It is widely known that NNs have powerful nonlinear fitting capabilities. Therefore, researchers have proposed the use of NNs to compensate for fiber nonlinearity, including various types of NNs such as artificial neural networks (ANNs) [86], long short-term memory networks (LSTMs) [87], and others, showing a better performance compared to traditional digital back-propagation and perturbation algorithms. LSTMs are a specific type of recurrent NN (RNN) model designed to mitigate the vanishing gradient problem commonly encountered in traditional RNNs.…”

Section: Fiber Nonlinearitymentioning

confidence: 99%

“…LSTMs have proven to be effective tools for mitigating transmission impairments, including both linear and nonlinear distortions, making them valuable for various applications in signal processing and communication systems. In [87], a linear network-assisted LSTM is proposed to mitigate the fiber nonlinearity in the wavelength-division-multiplexing (WDM) SSB system. Figure 4 depicts the architecture of a linear network-assisted LSTM.…”

Section: Fiber Nonlinearitymentioning

confidence: 99%

“…Figure 4 depicts the architecture of a linear network-assisted LSTM. The output 𝑦 ̂ can be expressed as [87]:…”

Section: Fiber Nonlinearitymentioning

confidence: 99%

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Driven by emerging technologies such as the Internet of Things, 4K/8K video applications, virtual reality, and the metaverse, global internet protocol traffic has experienced an explosive growth in recent years. The surge in traffic imposes higher requirements for the data rate, spectral efficiency, cost, and power consumption of optical transceivers in short-reach optical networks, including data-center interconnects, passive optical networks, and 5G front-haul networks. Recently, a number of self-coherent detection (SCD) systems have been proposed and gained considerable attention due to their spectral efficiency and low cost. Compared with coherent detection, the narrow-linewidth and high-stable local oscillator can be saved at the receiver, significantly reducing the hardware complexity and cost of optical modules. At the same time, machine learning (ML) algorithms have demonstrated a remarkable performance in various types of optical communication applications, including channel equalization, constellation optimization, and optical performance monitoring. ML can also find its place in SCD systems in these scenarios. In this paper, we provide a comprehensive review of the recent progress in SCD systems designed for high-speed optical short- to medium-reach transmission links. We discuss the diverse applications and the future perspectives of ML for these SCD systems.

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6.4Tb/s SSB WDM Transmission Over 320km SSMF With Linear Network-Assisted LSTM

Cited by 10 publications

References 9 publications

Research on short-term power load forecasting of smart grid based on residual DBSCAN-CNN-LSTM model

Research on short-term power load forecasting of smart grid based on residual DBSCAN-CNN-LSTM model

Machine Learning for Self-Coherent Detection Short-Reach Optical Communications

An efficient DDoS attack detection and prevention model using fusion heuristic enhancement of deep learning approach in FANET sector

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