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Joint estimation model for FSO channel parameters and performance evaluation based on CNNs.

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    This study introduces a novel convolutional neural network (CNN) model to accurately estimate noise and fading in free space optical (FSO) communication systems. The CNN joint estimator outperforms traditional methods, especially in noisy conditions.

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Free space optical (FSO) communication systems are susceptible to turbulence-induced fading, impacting signal reliability.
    • Existing channel estimation techniques often overlook receiver detection noise, leading to significant estimation errors.
    • Adaptive transmission strategies require accurate channel state information (CSI) for optimal performance.

    Purpose of the Study:

    • To propose a joint estimation model using convolutional neural networks (CNNs) for simultaneously estimating detection noise and turbulence fading parameters in FSO systems.
    • To address the limitations of conventional methods that neglect the impact of receiver noise on channel estimation accuracy.

    Main Methods:

    • Developed a joint estimation model leveraging CNNs to process turbulence channel simulation data, incorporating background detection noise.
    • Generated simulation datasets based on the edge probability distribution function of the received signal.
    • Trained the CNN estimator using techniques such as maximum pooling, adaptive learning rates, and regularization for optimal parameter estimation.

    Main Results:

    • The proposed CNN joint estimator demonstrated superior performance in environments with high detection noise compared to traditional maximum likelihood estimators.
    • The model exhibited enhanced generalization capabilities across various simulated atmospheric conditions.
    • Accurate estimation of channel characteristics was achieved through the optimized CNN network output.

    Conclusions:

    • The CNN-based joint estimation model offers a robust solution for mitigating turbulence-induced fading and detection noise in FSO communications.
    • This approach significantly improves the accuracy and reliability of channel state information estimation, particularly under adverse noise conditions.
    • The developed method holds promise for enhancing the performance and stability of future FSO communication systems.