Frequency Hopping Synchronization by Reinforcement Learning for Satellite Communication System
This work addresses synchronization challenges for tactical satellite communications, offering incremental improvements over existing methods like LSTM-based approaches.
The paper tackles the problem of inefficient frequency hopping synchronization in tactical satellite communication systems by proposing a method combining serial search and reinforcement learning, which reduces the average number of hops for synchronization by 58.17% and mean squared error by 76.95% compared to conventional methods.
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%.