A sparse wavelength aware learning framework for robust FSO channel estimation
S Senthilkumar, R Balakrishnan, M Irshad Ahamed, T Senthil Kumar · 2025
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Description
Senthilkumar and three co-authors wrote a 2025 paper on a learning system that estimates free-space optical channels. Optical links through open air offer huge bandwidth and license-free use, but turbulence, misalignment, and wavelength-dependent fading make their channels hard to predict. Classic estimators adapt too slowly when conditions swing.
Their network, SWALNet, uses a sparse attention encoder that stays aware of wavelength. It learns how each wavelength fades and how modulation distorts the signal, then maps the received waveform to channel coefficients. Training data simulated standard turbulence models plus pointing errors across several wavelengths. The network's channel error beat conventional baselines in simulation. Robust channel estimates keep optical links viable in bad air. The authors pitch the system for next-generation optical wireless networks.
Catalog record
| CREATOR | S Senthilkumar, R Balakrishnan, M Irshad Ahamed, T Senthil Kumar |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 62 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |