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Updated: Jun 11, 2026

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
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Published on: January 30, 2020

Data-driven statistical channel estimation for gamma-gamma noise.

Prerna Chaudhary, B R Manoj, Isha Chauhan

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    This summary is machine-generated.

    This study introduces new methods for accurate channel estimation in free-space optical (FSO) links facing interference. The proposed estimators improve performance by accounting for atmospheric turbulence and jamming, outperforming traditional methods.

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Wireless Communication Systems

    Background:

    • Free-space optical (FSO) links are susceptible to atmospheric turbulence and intentional jamming.
    • Accurate channel estimation is crucial for reliable FSO communication, especially in hostile environments.
    • Existing estimation methods often assume Gaussian noise, which is inadequate for impulsive jamming interference.

    Purpose of the Study:

    • To develop robust pilot-aided channel estimation techniques for FSO links under atmospheric turbulence and jamming.
    • To derive maximum likelihood (ML) and maximum a posteriori (MAP) estimators that account for gamma-gamma fading and non-Gaussian interference.
    • To evaluate the performance of the proposed estimators against conventional baselines.

    Main Methods:

    • Derivation of an exact likelihood function for the FSO channel with gamma-gamma fading and Bernoulli jammer activity.
    • Development of steepest-descent ML and MAP estimators incorporating gamma-gamma priors.
    • Extensive Monte Carlo simulations to assess Mean Square Error (MSE) and Bit Error Rate (BER) under varying conditions.

    Main Results:

    • The proposed ML and MAP estimators consistently achieve lower MSE and BER compared to Gaussian-assumed and mean-based methods.
    • Performance shows graceful degradation with increased jammer activity and significant gains with improved signal-to-jamming ratio (SJR).
    • The estimators demonstrate robustness by accurately modeling gamma-gamma statistics, outperforming Gaussian surrogates.

    Conclusions:

    • Accurate modeling of gamma-gamma fading statistics is critical for robust channel estimation in FSO systems facing jamming.
    • The developed gradient-based estimators offer practical and effective solutions for hostile FSO environments.
    • The proposed methods provide a significant improvement in estimation accuracy and reliability for FSO communications.