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Improving decryption quality of optical chaos communication using neural networks.
Optics Letters
|August 2, 2024
Summary
A novel neural network approach using convolutional neural networks (CNN) and bidirectional long short-term memory (LSTM) significantly improves secure optical chaos communication. This method reduces bit error rates (BER) for high-order signals after long-distance fiber transmission.
Area of Science:
- Optoelectronics
- Secure Communications
- Artificial Intelligence in Photonics
Background:
- Optical chaos communication offers high-speed, secure data transmission compatible with existing fiber optics.
- Fiber optic transmission impairments degrade chaotic synchronization, impacting information recovery, particularly for high-order modulated signals.
Purpose of the Study:
- To investigate the efficacy of a hybrid convolutional neural network (CNN) and bidirectional long short-term memory (LSTM) model in mitigating decryption bit error rates (BER) in optical chaos communication.
- To analyze the influence of neural network parameters and chaos synchronization coefficients on system performance.
Main Methods:
- Implementation of a CNN-LSTM neural network architecture for signal decryption.
- Synchronization of semiconductor lasers induced by a common signal.
- Experimental validation of the CNN-LSTM model's performance under simulated fiber optic transmission impairments.
Main Results:
- The CNN-LSTM model successfully reduced the BER for 16-ary quadrature-amplitude-modulation (16QAM) signals transmitted over 100 km of optical fiber.
- The post-transmission BER was reduced from an initial 3.05 × 10-2 to below the soft-decision forward-error-correction (SD-FEC) threshold of 2.0 × 10-2.
Conclusions:
- The proposed CNN-LSTM approach effectively enhances the robustness and reliability of optical chaos communication systems against fiber optic transmission impairments.
- This AI-driven decryption method shows significant promise for improving the quality of recovered information in high-order modulated signals within secure optical communication systems.
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