用强化学习提升卫星通信跳频同步效率，大幅降低误码与搜寻次数。
Frequency Hopping Synchronization by Reinforcement Learning for Satellite Communication System
- 结合串行搜索与强化学习，分阶段实现粗同步与精同步。
- 相较传统方法，平均跳频次数减少58.17%，时序估计误差降低76.95%。
- 适用于高动态、强干扰的战术卫星通信场景，适合抗干扰系统设计者。
用于战术目的的卫星通信系统（SCS）需具备强安全性和抗干扰能力，跳频（FH）技术为此提供了有效方案。然而，现有跳频系统因其他设备干扰及卫星通信固有的显著路径损耗，常出现同步错位，导致通信效率低下。传统方法如基于长短期记忆网络（LSTM）虽有改进，但在动态卫星环境中仍表现不佳。本文提出一种新方法，通过串行搜索实现粗同步，结合强化学习完成精同步，显著提升跳频信号同步性能。数学分析与仿真结果表明，相比传统串行搜索法，该方法使同步所需平均跳频次数减少58.17%，上行跳变定时估计均方误差（MSE）降低76.95%；相较基于串行搜索与LSTM的早迟门同步法，平均跳频次数减少12.24%，MSE下降18.5%。
原文摘要 · Abstract (English)
Satellite communication systems (SCSs) used for tactical purposes require robust security and anti-jamming capabilities, making frequency hopping (FH) a powerful option. However, the current FH systems face challenges due to significant interference from other devices and the considerable path loss inherent in satellite communication. This misalignment leads to inefficient synchronization, crucial for maintaining reliable communication. Traditional methods, such as those employing long short-term memory (LSTM) networks, have made improvements, but they still struggle in dynamic conditions of satellite environments. This paper presents a novel method for synchronizing FH signals in tactical SCSs by combining serial search and reinforcement learning to achieve coarse and fine acquisition, respectively. The mathematical analysis and simulation results demonstrate that the proposed method reduces the average number of hops required for synchronization by 58.17% and mean squared error (MSE) of the uplink hop timing estimation by 76.95%, as compared to the conventional serial search method. Comparing with the early late gate synchronization method based on serial search and use of LSTM network, the average number of hops for synchronization is reduced by 12.24% and the MSE by 18.5%.
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