A Novel Demodulation and Estimation Algorithm for Blackout Communication: Extract Principal Components with Deep Learning

TL;DR

This paper introduces a deep learning-based algorithm called symmetric manifold network (SMN) that jointly demodulates and estimates channels in blackout communication, significantly reducing error rates and improving bandwidth efficiency.

Contribution

The paper presents a novel deep learning algorithm that jointly performs demodulation and channel estimation for blackout communication using principal curve analysis.

Findings

Significantly reduces symbol error rate compared to existing methods.

Enables accurate fading estimation with high bandwidth utilization.

Demonstrates effectiveness through simulation results.

Abstract

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.