First Name
Xiaojing
Last Name
Gao
Dataset Entries from this Author
The source data files and code files of the paper: optical chaos shift keying communication system via neural network-based signal reconstruction. The following data is included:
1. Source figure file in the paper;
2. Source code of the proposed scheme, include the simulation code for communication, secure analysis and parameter mismatch range.
3. The source Simulink module is included for time-delayed chaotic signal generation.
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The data was generated using two types of systems: one based on opto-electronic oscillators (OEO) that produce chaos through external nonlinear opto-electronic time-delayed feedback, and the other built on chaotic lasers. This dataset operates at a 40 GHz data rate, showcasing high-speed signal generation and its potential for applications in high-bandwidth communication and advanced signal processing.
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This dataset contains comprehensive simulation results for an optical chaos communication (OCC) system, focusing on the performance analysis and security evaluation of various neural network-based demodulation techniques.
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The optical chaos communication (OCC) can provide physical layer security for high-speed data transmission. In these OCC systems, the time delay signature (TDS) serves as a crucial encryption key. We propose a method based on reservoir computing (RC) network for TDS extraction of OCC systems. The mapping relationship between the system output time series and its delay variants is learned by the RC network. Then, the convergence performance of the RC network is measured and used for TDS extraction. The effectiveness is verified by extracting TDS of the two main types
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The optical chaos communication (OCC) can provide physical layer security for high-speed data transmission. In these OCC systems, the time delay signature (TDS) serves as a crucial encryption key. We propose a method based on reservoir computing (RC) network for TDS extraction of OCC systems. The mapping relationship between the system output time series and its delay variants is learned by the RC network. Then, the convergence performance of the RC network is measured and used for TDS extraction. The effectiveness is verified by extracting TDS of the two main types
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