arXiv subjects

Rachel Wang

Publications and source records attributed to Rachel Wang.

5 recordsLinked to original sources

arXiv2026

Statistical study of energy dissipation in magnetic structures during turbulent reconnection in the Earth's magnetotail

Magnetic reconnection is a ubiquitous plasma phenomenon that plays a critical role in particle heating and energization. During reconnection, the topology of magnetic field rearranges, depositing energy into the surrounding plasma through bulk flow, thermal heating, or non-thermal particle acceleration. While the pathways of this transformation from magnetic energy into kinetic have been studied extensively in recent years through theoretical or case-by-case observations, comprehensive statistical studies remain limited. In this paper, we present a statistical investigation using data from the Magnetospheric Multiscale (MMS) mission, and detail the particle energization mechanisms in magnetic structures found near reconnecting regions in turbulent Earth's magnetotail. We find that electrons with motion perpendicular to the magnetic field dominate $\vec{j}\cdot\vec{E}$ dissipation. In contrast to the conventional picture of unidirectional energy transfer to particles by laminar two-dimensional (2D) reconnection, we find that energy exchange within magnetic structures during turbulent reconnection tends to be bidirectional with only a small positive bias from electromagnetic fields to particles. Specific electron energization mechanisms are quantified, including those due to parallel electric field, Fermi energization from curvature drift, betatron heating from magnetic field inhomogeneity, and polarization drift.

physics.space-ph↗

arXiv2025

AI/ML based Joint Source and Channel Coding for HARQ-ACK Payload

Channel coding from 2G to 5G has assumed the inputs bits at the physical layer to be uniformly distributed. However, hybrid automatic repeat request acknowledgement (HARQ-ACK) bits transmitted in the uplink are inherently non-uniformly distributed. For such sources, significant performance gains could be obtained by employing joint source channel coding, aided by deep learning-based techniques. In this paper, we learn a transformer-based encoder using a novel "free-lunch" training algorithm and propose per-codeword power shaping to exploit the source prior at the encoder whilst being robust to small changes in the HARQ-ACK distribution. Furthermore, any HARQ-ACK decoder has to achieve a low negative acknowledgement (NACK) error rate to avoid radio link failures resulting from multiple NACK errors. We develop an extension of the Neyman-Pearson test to a coded bit system with multiple information bits to achieve Unequal Error Protection of NACK over ACK bits at the decoder. Finally, we apply the proposed encoder and decoder designs to a 5G New Radio (NR) compliant uplink setup under a fading channel, describing the optimal receiver design and a low complexity coherent approximation to it. Our results demonstrate 3-6 dB reduction in the average transmit power required to achieve the target error rates compared to the NR baseline, while also achieving a 2-3 dB reduction in the maximum transmit power, thus providing for significant coverage gains and power savings.

eess.SP↗

arXiv2025

Demonstrating Interoperable Channel State Feedback Compression with Machine Learning

Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, where the user equipment (UE) and base station have no access to each others' ML models. In this paper, we present a novel approach for training interoperable compression and decompression ML models in a confidential manner, and demonstrate the accuracy of the ensuing models using prototype UEs and base stations. The performance of the ML-based channel feedback is measured both in terms of the accuracy of the reconstructed channel information and achieved downlink throughput gains when using the channel information for beamforming. The reported measurement results demonstrate that it is possible to develop an accurate ML-based channel feedback link without having to share ML models between device and network vendors. These results pave the way for a practical implementation of ML-based channel feedback in commercial 6G networks.

eess.SP↗

arXiv2023

Solar wind current sheets: MVA inaccuracy and recommended single-spacecraft methodology

We present the analysis of 2,033 current sheets (CS) observed aboard four Cluster spacecraft in a pristine solar wind. Four-spacecraft estimates of the CS normal and propagation velocity are compared with different single-spacecraft estimates. The Minimum Variance Analysis (MVA) of the magnetic field is shown to be highly inaccurate in estimating the normal. The MVA normal often differs by more than 60 degrees from the normal obtained by multi-spacecraft timing method, likely due to ambient turbulent fluctuations. In contrast, the cross-product of magnetic fields at the CS boundaries delivers the normal with the uncertainty of less than 15 degrees at the confidence level of 90%. The CSs are essentially frozen into plasma flow, since their propagation velocity is consistent with local ion flow velocity within 20% at the confidence level of 90%. The single-spacecraft methodology based on the cross-product method and frozen-in assumption delivers the CS thickness and current density amplitude within 20% of their actual values at the confidence level of 90%. The CSs are kinetic-scale structures with half-thickness $\lambda$ from a few tenths to tens of local proton inertial length $\lambda_{p}$ and scale-dependent shear angle and current density amplitude, $\Delta \theta \propto (\lambda/\lambda_p)^{0.5}$ and $J_0\propto (\lambda/\lambda_{p})^{-0.5}$. The classification of the CSs in terms of tangential and rotational discontinuities remains a challenge, because even the four-spacecraft normal has too large uncertainties to reveal the actual normal magnetic field component. The presented results will be valuable for the analysis of solar wind CSs, when only single-spacecraft measurements are available.

physics.space-ph↗

arXiv2022

Double layers in the Earth's bow shock

We present Magnetospheric Multiscale observations of electrostatic double layers in quasi-perpendicular Earth's bow shock. These double layers have predominantly parallel electric field with amplitudes up to 100 mV/m, spatial widths of 50-700 m, and plasma frame speeds within 100 km/s. The potential drop across a single double layer is 2-7% of the cross-shock potential in the de Hoffmann-Teller frame and occurs over the spatial scale of ten Debye lengths or one tenth of electron inertial length. Some double layers can have spatial width of 70 Debye lengths and potential drop up to 30% of the cross-shock potential. The electron temperature variation observed across double layers is roughly consistent with their potential drop. While electron heating in the Earth's bow shock occurs predominantly due to the quasi-static electric field in the de Hoffmann-Teller frame, these observations show that electron temperature can also increase across Debye-scale electrostatic structures.

physics.space-ph↗