A New Meta-Learning Framework for Estimating Atmospheric Turbulence and Phase Noise in Optical Satellite Internet of Things Systems

With the advantages of super-high-transmission rate, anti-electromagnetic interference and good confidentiality, optical wireless communication (OWC) systems play an important role in satellite Internet of Things (IoT). In this article, we propose a new meta-learning-based channel estimation scheme (meta-CE) to address the challenges of atmospheric turbulence and phase distortion in satellite OWC links. A neural network-based channel estimator outputs channel parameters, rather than categories, and utilizes meta-learning to enhance its convergence speed and adaptability to new environments. The meta-CE exhibits superior estimation accuracy, fast convergence, and generalization, compared to baseline schemes, and even outperforms the minimum mean square error (MMSE) channel estimation, especially with short pilot symbols, low-signal-to-noise ratio (SNR) and severe turbulence. In a $4 \times 4$ multiple-input–multiple-output (MIMO) scheme with 8-bit pilot symbols and 0-dB SNR, the mean square error of the meta-CE is about 35% lower than that of the MMSE in a strong Gamma-Gamma turbulence channel.