Dynamic Data Selection-Aided Channel Estimation for mmWave LEO Communications in NTNs

Non-terrestrial networks (NTNs), connecting components such as low-earth orbit (LEO) satellites, exhibit highly dynamic behaviors in both their topological configurations and radio channels. Traditional pilot-aided channel estimation (PACE) methods are susceptible to significant Doppler shifts and rapid channel variations, limiting further enhancements in data transmission rates or incurring unaffordable pilot overheads. Data-aided channel estimation (DACE), as a potential improvement path, leverages data vectors to supplement pilot signals. Nevertheless, conventional DACE faces difficulties in effectively selecting data vectors under frequency offsets and rapid channel variations. To address this, this paper introduces a dynamic threshold-based scheme for selecting reliable data vectors, which leverages an adaptive threshold to precisely identify reliable data vectors within a frame. Furthermore, we propose a data vector deviation estimation method as a preprocessing step to correct the overall offset of data vectors, thereby ensuring the efficacy of data selection even amidst significant channel variations. Our approach significantly elevates the performance of LEO links within current protocols and is compatible with higher modulation orders and frequency bands. Simulation results validate the effectiveness of the proposed scheme.