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A sparse wavelength aware learning framework for robust FSO channel estimation.

S Senthilkumar1, R Balakrishnan2, M Irshad Ahamed3

  • 1Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. senthilkumar.s@egspec.org.

Scientific Reports
|December 10, 2025
PubMed
Summary
This summary is machine-generated.

A new Sparse Wavelength-Aware Learning Network (SWALNet) improves Free Space Optical (FSO) communication by accurately estimating channel conditions. This deep learning approach enhances signal quality and spectral efficiency in challenging atmospheric environments.

Keywords:
Atmospheric turbulenceChannel predictionFSO estimationOFDM modulationOptical signal fadingSparse learningWavelength diversity

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Area of Science:

  • Optical Wireless Communication
  • Free Space Optics (FSO)
  • Deep Learning for Communications

Background:

  • FSO systems offer high bandwidth and security but suffer from atmospheric turbulence and misalignment.
  • Traditional channel estimation methods (LMS, RLS) lack adaptability for dynamic FSO conditions.
  • Wavelength-specific fading and modulation distortions degrade FSO signal predictability.

Purpose of the Study:

  • To introduce a novel deep learning architecture, SWALNet, for precise FSO channel estimation.
  • To address limitations of conventional methods in handling atmospheric turbulence and wavelength variations.
  • To improve signal quality and spectral efficiency in FSO communication systems.

Main Methods:

  • Developed a Sparse Wavelength-Aware Learning Network (SWALNet) utilizing an attention-based sparse encoder.
  • SWALNet dynamically learns wavelength-specific impact patterns for distorted OFDM signals.
  • Evaluated the model using a dataset simulating Gamma-Gamma turbulence, pointing error, and wavelength diversity.

Main Results:

  • Achieved a Mean Squared Error of 0.0037, Bit Error Rate of 1.24 × 10-3, and Q-Factor of 14.68 dB.
  • Demonstrated superior channel estimation performance compared to LMS, Kalman filter, and standard DNN models.
  • Showcased significant error reduction and enhanced spectral efficiency across various modulation schemes.

Conclusions:

  • SWALNet effectively captures and compensates for modulation-induced distortions and wavelength-dependent fading in FSO links.
  • The proposed deep learning model offers precise and adaptive channel estimation for optical wireless communications.
  • SWALNet enhances the reliability and performance of FSO systems under adverse atmospheric conditions.