Haoyan Liu

During hypersonic reentry, the dynamically evolving plasma sheath around the vehicle induces severe, time-varying attenuation of electromagnetic waves, leading to rapidly fluctuating propagation loss on the communication link. Consequently, the received signal-to-noise ratio (SNR) fluctuates severely and may even drop to a communication outage; under packetized transmission, this results in a time-varying plasma-induced packet-erasure process. Such harsh and rapidly changing channels make reliable information delivery extremely challenging. Conventional fountain-coded transmission is well-suited to plasma-induced packet erasures; however, it often requires a large transmission overhead to achieve successful recovery, resulting in low efficiency under limited overhead budgets. In practice, reentry tasks typically impose a strict overhead budget due to short contact time and limited link resources, making such excessive redundancy undesirable. Meanwhile, many transmission payloads are inherently distortion-tolerant, suggesting that early usable reconstruction is often more valuable than perfect recovery. On this basis, we propose a low-overhead fountain-aided progressive transmission framework for reentry plasma sheath channels. The proposed scheme combines: 1) an importance-guided unequal-protection Luby transform (IG-UP-LT) coding strategy to accelerate the recovery of core information under packet erasures and 2) a neural progressive encoder–decoder (PED) that produces an importance-ordered set of latent features to support progressive reconstruction at the receiver. With a task-defined threshold, the receiver can terminate transmission once the reconstruction becomes sufficiently usable, thereby avoiding unnecessary overhead. Using laboratory-measured SNR traces under mild, moderate, and severe plasma regimes, extensive Monte Carlo simulations demonstrate that the proposed framework substantially reduces the overhead required to meet the target reconstruction quality and achieves a high probability of successful reconstruction under strict overhead constraints.

The absorption characteristics of the plasma sheath have a significant impact on the propagation of electromagnetic (EM) waves. The high-speed motion of hypersonic vehicles, combined with multipath transmission, further intensifies the selective fading of reentry plasma sheath channels in the time-frequency (TF) domain. These effects can severely degrade communication quality and, in extreme cases, lead to complete radio blackout. Recently, a novel modulation technique—orthogonal time-frequency space (OTFS)—has been proposed to enable reliable communication in high-mobility environments by operating in the delay-Doppler (DD) domain. However, the nonstationary flow field around reentry vehicles causes fluctuations in plasma sheath parameters, resulting in rapid time-varying (RTV) fading. To date, no studies have explored the behavior of such channels in the DD domain, which is essential for the effective deployment of OTFS in this context. This article presents a theoretical analysis demonstrating that RTV fading in reentry plasma sheath channels induces Doppler-axis energy leakage in the DD domain channel impulse response (CIR), with a leakage pattern that aligns with the spectral characteristics of the time-varying fading coefficients. In addition, we analyze the impact of two dominant dynamic factors in such channels—time-varying electron density and time-varying Doppler shift—on the CIR. Furthermore, we show that the parameter configuration of the OTFS system also plays a critical role in shaping the CIR. Finally, we verify the performance of the OTFS system through simulations, confirming its potential for robust communication under extreme reentry conditions.

Reentry plasma sheath diagnostic plays a crucial role in the research of the blackout problem. The parameters of the plasma sheath can be described using an inhomogeneous distribution, and this distribution affects the characteristics of the multi-point peaks in the reflection data. This paper accurately captures the correlation between plasma parameter distribution and multi-point peaks by extracting key features of the multi-point peaks and proposes a plasma parameter inversion method based on neural networks. It achieves a method for fast and accurate diagnosis of plasma electron density distribution and collision frequency through reflection data.

-dynamic plasma sheath can severely interfere with the communication of hypersonic vehicles during atmospheric reentry. Theoretical and experimental results show that low-frequency (LF) electromagnetic (EM) waves could penetrate the plasma sheath, building a feasible method to solve the “radio blackout” problem. This paper discovers that the propagation of LF EM waves in plasmas is still influenced by parasitic modulation effects. Compared to microwave frequencies, the impact of parasitic modulation effects on signal modulation patterns is more distinct for LF EM waves. In contrast to the microwave frequency range, where the rotation direction of QPSK signal constellation points changes with the ratio of plasma frequency to electromagnetic wave frequency, in the LF range, the constellation points undergo limited clockwise rotation. This phenomenon can be attributed to the unique magnetic field propagation mechanism of LF EM waves in dynamic plasmas. This paper analyzes the mechanism of this specific parasitic modulation effect and discovers a sinusoidal transformation relationship between amplitude attenuation and phase shift. Meanwhile, the experimental and simulation results proved that the time-varying plasma could cause the parasitic modulation effect of LF EM wave, resulting in a limited clockwise rotation of orthogonal phase-shift keying constellation points, which is consistent with the theoretical analysis.

Orthogonal time frequency space (OTFS) modulation has been confirmed to provide significant performance advantages against Doppler in high-mobility scenarios. The core feature of OTFS is that the time-variant channel is converted into a non-fading 2D channel in the delay-Doppler (DD) domain so that all symbols experience the same channel gain. In available literature, the channel is assumed to be quasi-static over an OTFS frame. As for more practical scenarios, the Doppler shift is dynamic and the channel gain could be time-variant as the environment or medium changes. In this paper, we analyze the characterizations of OTFS modulation over a more general multipath channel, where the signal in each propagation path has experienced a unique rapid fading. First, we derive the explicit input-output relationship of OTFS in the DD domain for the case of ideal pulse and rectangular pulse. It is shown that the rapid fading will produce extra Doppler dispersion without impact on delay domain. We next demonstrate that OTFS can be interpreted as an efficient time diversity technology that combines space-time encoding and interleaving against the rapid fading. Simulation results reveal that OTFS is insensitive to rapid fading and still outperforms conventional multicarrier systems in these channels.

Performing as an important technology, microwave interferometry helps understand and analyze plasma characteristics. However, the relatively appropriate diagnosing methods being discovered for investigating fast time-varying plasma have met with limited success so far. The crucial problem of this task lies in eliminating the interference of additive noise while compensating for phase ambiguity of the initial point and of the phase jump points. In particular, randomly varying electron density and noise make it impossible to directly detect these points. Theoretical analysis has found that the time-varying electron density of plasma causes the received signal to rotate on the complex plane. Inspired by this fact, a deep learning (DL) method is proposed to track the time-varying plasma and solve the above two problems, during which the deep neural network (DNN) is utilized to extract the curve from the data, thereby eliminating the noise and detecting the initial point and the phase jump points according to the projection of the data on the curve. Experimental results suggest that the learned curve is in good consistency with the ground-truth curve. Meanwhile, the time-varying electron density and the collision frequency can be accurately diagnosed using the learned curve.

The phenomenon of radio blackout due to a plasma sheath during reentry has attracted much attention over the last several decades. However, radio blackout has long puzzled aerospace researchers and has not yet been completely resolved. Owing to the effects of the time-varying plasma sheath channel environment, the constellation of received signals exhibits multiple manifolds, making the traditional algorithms based on Euclidean distance unable to perform demodulation. In the current study, we proposed a novel demodulation method based on spectral clustering for phase-modulated signals interrupted by the plasma sheath channel. Experimental results revealed that as long as the receiving radio waves were above the floor of receiver's dynamic range, the proposed algorithm effectively classified received signals after time-varying plasma. The parasitic modulation interference caused by time-varying plasma was restrained effectively through the novel algorithm. In contrast to the traditional communication method, the proposed method can be applied to telemetry, track and command, as well as communication between reentry vehicles and near-space hypersonic vehicles in future.