Meta-Learning-Driven Photon Counting Multi-User Satellite Communications over Strong Atmospheric Turbulence Channels

Photon-counting constitute a promising technology for ultra-weak signal satellite communications. Considering the Poisson shot noise impairment, atmospheric turbulence fading, and multi-user interference, in this paper, a meta-learning-driven photon-counting multi-user single-input multiple-output (MU-SIMO) scheme is developed and analyzed. Referred to as meta-learning-driven signal detection (Meta-SD), this scheme can achieve rapid convergence with limited samples and significantly improve system detection performance. Simulation results demonstrate that the proposed meta-learning scheme outperforms the mean square error based signal detection (MSE-SD) baseline, in terms of detection accuracy, robustness to signal-dependent Poisson shot noise, convergence speed, and generalization to few-shot detection tasks with previously untrained signal classes. Specifically, Meta-SD achieves nearly a tenfold reduction in BER, compared with the derived MSE-SD benchmark, in a 4×8 MU-SIMO scenario at Es=−140 dBJ.