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- W4387491147 abstract "Optical Fiber Communication systems are increasingly used for transport networks due to the low cost, large capacity, and high bit rate of optical transmission [1]. However, effective traffic management and predicting future congestion levels is a critical challenge in order to ensure smooth and reliable operations. Machine learning algorithms can be used to accurately predict the future traffic volumes in such networks, providing real-time information on expected capacity usage and allowing operators to take proactive measures to prevent congestion. In this paper, we propose a novel Machine Learning based traffic prediction algorithm for Optical Fiber Communications Systems (OFCSs). The proposed algorithm uses a combination of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures for predicting future traffic patterns. We train the model using a sequence of historical traffic data from an OFCS and evaluate it against benchmarks in terms of prediction accuracy and processing time. The results show that the proposed algorithm can make accurate traffic predictions, with a mean absolute error (MAE) of 0.036 for one-step-ahead predictions and 0.077 for five-steps-ahead. Furthermore, the processing time is significantly less than other benchmark models, providing a practical and reliable approach for predicting future traffic in OFCSs. We also discuss the implications of this study in terms of improving optical fiber capacity utilization and meeting the increased demand of growing data traffic." @default.
- W4387491147 created "2023-10-11" @default.
- W4387491147 creator A5017566187 @default.
- W4387491147 date "2023-08-25" @default.
- W4387491147 modified "2023-10-16" @default.
- W4387491147 title "A New Traffic Prediction Algorithm with Machine Learning for Optical Fiber Communication System" @default.
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- W4387491147 doi "https://doi.org/10.1109/asiancon58793.2023.10270384" @default.
- W4387491147 hasPublicationYear "2023" @default.
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