A Novel Demodulation and Estimation Algorithm for Blackout Communication: Extract Principal Components with Deep Learning
This addresses a domain-specific problem for reentry or near-space communication systems, offering an incremental improvement over existing methods.
The paper tackles the problem of signal distortion in blackout communication caused by time-varying plasma sheath channels by proposing a deep learning algorithm called symmetric manifold network (SMN) for joint demodulation and channel estimation, which significantly reduces symbol error rate and enables accurate fading estimation with high bandwidth utilization.
For reentry or near space communication, owing to the influence of the time-varying plasma sheath channel environment, the received IQ baseband signals are severely rotated on the 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 a mass of bandwidth will be consumed to track the time-varying channel with traditional estimation. In this paper, motivated by principal curve analysis, we propose a deep learning (DL) algorithm which 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 channel 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 bandwith utilization rate.