Traditional channel estimation and coding control methods suffer from low estimation accuracy and slow response, making it difficult to meet the demands for efficient and stable communication. Therefore, a channel estimation method, based on meta-learning and a Back Propagation Neural Network, is proposed. An adaptive coding dynamic control algorithm, which combines Reinforcement Learning and Adaptive Modulation and Coding linkage, is developed to achieve fast and accurate channel state estimation and real-time dynamic adjustment of coding strategies. Experimental results show that, in terms of channel judgment accuracy, the proposed algorithm achieves up to 99%, outperforming the best performance of the comparison algorithm, which is 97%. The average spectral efficiency reaches 6.4 bps/Hz, significantly higher than the 4.2 bps/Hz of the comparison algorithm. Moreover, the spectral efficiency fluctuation range of the proposed algorithm is from 5.9 bps/Hz to 8 bps/Hz, showing better performance compared to the 3 bps/Hz minimum fluctuation range of the comparison algorithm. These results demonstrate that the proposed algorithm has significant advantages in channel estimation accuracy and coding control performance. It provides a new approach for efficient and stable transmission in satellite laser communication and contributes to the advancement of intelligent and efficient technology in this field.
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