Neural equalization for bit-error mitigation in atmospheric optical downlinks: implications for remote sensing instrument data integrity

This study investigates atmospheric channel effects on optical instrumentation and data retrieval, focusing on turbulence-induced scintillation and hardware-related nonlinear distortion impacting signals from space- or air-borne atmospheric sensors. Standard physical-layer countermeasures—advanced modulation formats, multiplexing, dispersion compensation, and forward error correction—are evaluated as baselines for maintaining signal fidelity.

A degraded atmospheric transmission channel is simulated in Python, in which a binary signal is OOK-modulated and corrupted by additive white Gaussian noise (SNR = 1, 3, 5, 7, 10 dB) combined with a sinusoidal nonlinear component to emulate transient atmospheric fading. Two recovery techniques are compared: a classical threshold detector and a lightweight multilayer perceptron (MLP) classifier using a sliding temporal window.

Results show comparable performance under severe noise (SNR = 1 dB: BER 0.2268 classical vs. 0.2301 MLP). However, as channel quality improves, the MLP consistently outperforms the threshold detector, achieving BERs of 0.1691 vs. 0.1807 at 3 dB and 0.0330 vs. 0.0549 at 10 dB—representing a 40% error reduction under clear-sky-equivalent conditions.

These results demonstrate that efficient neural architectures can learn nonlinear atmospheric distortion patterns without explicit propagation models, offering an adaptive correction technique for variable turbulence profiles. This capability enhances the quality of retrieved atmospheric parameters (e.g., aerosol/cloud properties). Future work will extend this to recurrent networks for sequential scintillation modeling, integrating AI-based equalizers directly into instrument processing chains to improve retrieval accuracy for next-generation atmospheric lidar and spectrometer payloads.