A Joint Demodulation and Estimation Algorithm for Plasma Sheath Channel: Extract Principal Curves With Deep Learning

Abstract

For reentry communication, owing to the influence of the time-varying plasma sheath, the received IQ baseband signals are severely rotated on constellation. Researches have shown that the frequency of electron density varies from 20kHz to 100 kHz which is on the same order as the symbol rate of most TT&C communication systems and traditional estimation algorithms cannot track the variation of channel. In this letter, motivated by principal curve analysis, we propose a deep learning (DL) algorithm which is called symmetric manifold network (SMN) to extract the curves on the constellation and classify the signals based on the curves. The key advantage is that SMN can achieve joint optimization of demodulation and estimation. From our simulation results, the new algorithm significantly reduces the symbol error rate (SER) compared to existing algorithms and enables accurate estimation of fading with extremely high bandwidth utilization rate.