LSTM-Based Adaptive Modulation and Coding for Satellite-to-Ground Communications

Satellite communication develops rapidly due to its global coverage and is unrestricted to the ground environment. However, compared with the traditional ground TCP/IP network, a satel-lite-to-ground link has a more extensive round trip time (RTT) and a higher packet loss rate, which takes more time in error recovery and wastes precious channel resources. Forward error cor-rection (FEC) is a coding method that can alleviate bit error and packet loss, but how to achieve high throughput in the dynamic network environment is still a significant challenge. Inspired by the deep learning technique, this paper proposes a signal-to-noise ratio (SNR) based adaptive coding modulation method. This method can maximize channel utilization while ensuring communication quality and is suitable for satellite-to-ground communication scenarios where the channel state changes rapidly. We predict the SNR using the long short-term memory (LSTM) network that con-siders the past channel status and real-time global weather. Finally, we use the optimal matching rate (OMR) to evaluate the pros and cons of each method quantitatively. Extensive simulation results demonstrate that our proposed LSTM-based method outperforms the state-of-the-art predic-tion algorithms significantly in mean absolute error (MAE). Moreover, it leads to the least spec-trum waste.