A sparse wavelength aware learning framework for robust FSO channel estimation

The rapid evolution of optical wireless communication technologies, particularly Free Space Optical (FSO) systems, presents a compelling alternative to radio-frequency communication due to their inherent advantages such as higher bandwidth, enhanced security, and license-free spectrum utilization. However, FSO links are highly susceptible to atmospheric turbulence, beam misalignment, and wavelength-specific attenuation, which severely degrade signal quality and channel predictability. Traditional estimation techniques such as LMS and RLS offer limited adaptability under rapidly varying conditions, often leading to inadequate compensation. To address these limitations, a novel deep learning architecture Sparse Wavelength-Aware Learning Network (SWALNet) is proposed to capture modulation-induced distortions and wavelength-dependent fading through an integrated attention-based sparse encoder. The proposed SWALNet dynamically learns wavelength-specific impact patterns and maps distorted OFDM signals to accurate channel coefficients. The proposed model is evaluated using dataset which is developed based on Gamma-Gamma turbulence, pointing error, with different wavelength diversity. Simulations experimentations validated the proposed model superior performance through its achieved Mean Squared Error of 0.0037, Bit Error Rate of 1.24 × 10^-3, and Q-Factor of 14.68 dB. The results clearly indicate the precise channel estimation performance of proposed model over conventional LMS, Kalman filter, and DNN models. The results demonstrate the proposed SWALNet model significant reduction in error estimation and enhanced spectral efficiency across multiple modulation schemes.