Data-Assisted Channel Estimation for Non-Terrestrial Network in Low SNR
Zhuohang Li, Yitong Liu, Yunan Sun, Huan Kong, Gang Li, Luhan Wang, Hongwen Yang · 2024
The coverage enhancement of satellite communications has become a critical topic in communication research. In this paper, we propose a data-assisted channel estimation method applied to Non-Terrestrial Networks (NTN) in 5G New Radio (NR) systems. Firstly, we explore the challenges posed by the unique characteristics of NTN, such as low signal-to-noise ratios (SNR), higher Doppler shifts and longer delay spreads. Then we discuss the system transmission process and common channel estimation methods. Based on this, we propose a data-assisted channel estimation method to enhance the accuracy of channel estimation under low SNR conditions for NTN. This method integrates an iterative process that leverages received data symbols to refine channel estimates, thereby improving decoding performance. Subsequently, we simulate the performance of the proposed data-assisted channel estimation method. Simulation results demonstrate the effectiveness of proposed method under various SNR conditions and channel configurations.