arXiv subjects

Yueqing Wang

Publications and source records attributed to Yueqing Wang.

9 recordsLinked to original sources

arXiv2026

Multi-Domain Iterative Detection for Massive Connectivity in LEO Satellite Networks

Grant-Free (GF) random access is promising for low Earth orbit satellite Internet due to its reduced access latency. However, existing schemes suffer from poor performance in massive connectivity scenarios. To address this challenge, we firstly propose an iterative residual feedback multi-measurement vector approximate message passing algorithm. This algorithm leverages multi-domain synergistic sparsity in the spatial-frequency and angular-delay domains to alternately perform active user terminal detection (AUD) and channel estimation (CE). Additionally, a residual feedback mechanism is incorporated to suppress error accumulation, thereby enhancing AUD performance. Furthermore, conventional data detection (DD) methods significantly degrade when active user terminals are spatially close or outnumber the satellite's receive antennas, making the demodulation problem rank-deficient or underdetermined. To mitigate this, we design a data modulation scheme via joint spatial-frequency multi-domain spreading, which utilizes observations from both spatial and frequency domains to facilitate multi-domain DD. Simulation results demonstrate that the proposed scheme significantly outperforms existing GF methods in terms of AUD accuracy, CE precision, and bit error rate, especially under conditions of low effective pilot length and practical signal-to-noise ratios.

eess.SP↗

arXiv2026

MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation

Recent research has shown that unsourced massive access (UMA) is naturally well-suited for over-the-air computation (AirComp), as it does not require knowledge of each individual signal, as demonstrated by the massive digital AirComp (MD-AirComp) scheme proposed in prior work. The MD-AirComp scheme has proven effective in federated edge learning and is highly compatible with current digital wireless networks. However, it depends on channel pre-equalization, which may amplify computation errors in the presence of channel estimation inaccuracies, thus limiting its practical use. In this paper, we propose a blind MD-AirComp+ scheme, which takes advantage of the channel hardening effect in massive multiple-input multiple-output (MIMO) systems. We provide an upper bound on the computation mean square error, analyze the trade-off between computation accuracy and communication overhead, and determine the optimal quantization level. Additionally, we introduce a deep unfolding algorithm to reduce the computational complexity of solving the underdetermined detection problem formulated as a least absolute shrinkage and selection operator optimization problem. Simulation results confirm the effectiveness of the proposed MD-AirComp+ framework, the optimal quantization selection strategy, and the low-complexity detection algorithm.

eess.SP↗

arXiv2026

UniField: Joint Multi-Domain Training for Universal Surface Pressure Modeling

Accurate modeling of surface pressure fields around objects is fundamental to aerodynamic analysis and design. While neural networks have shown promise as efficient alternatives to expensive Computational Fluid Dynamics (CFD) simulations, their applicability is often constrained by data scarcity and poor generalization across different aerodynamic domains. To address these challenges, we propose UniField, a unified framework that enables joint training across multiple aerodynamic domains including automobiles, trains, aircraft. UniField employs a shared geometry encoder to extract domain-agnostic representations from surface point clouds, and integrates domain-specific flow information through Parallel Flow-Conditioned Adaptive LayerNorm (PFC-AdaLN). In addition to consolidating existing datasets from specialized research field including automobiles, trains and aircraft, we further introduce ThingiCFD, a large-scale CFD dataset constructed from Thingi10k geometries with extensive flow condition randomization, substantially expanding geometric and flow diversity during training. UniField achieves SOTA performance on the public DrivAerNet++ benchmark. In addition, our experiments demonstrate that joint multi-domain training consistently improves surface pressure prediction accuracy, particularly in data-scarce domains. These results highlight the potential of UniField as a foundation model for data-driven aerodynamic modeling. Code and data will be available at https://github.com/zoujunhong/UniField.

cs.CE↗

arXiv2025

Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs' activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://github.com/owenwang517/tvt-2025.

eess.SP↗

arXiv2023

Novel deep learning methods for 3D flow field segmentation and classification

Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow. Existing deep learning methods mainly based on global information and focused on 2D circumstance. Based on flow field theory, we propose novel flow field segmentation and classification deep learning methods in three-dimensional space. We construct segmentation criterion based on local velocity information and classification criterion based on the relationship between local vorticity and vortex wake, to identify vortex structure in 3D flow field, and further classify the type of vortex wakes accurately and rapidly. Simulation experiment results showed that, compared with existing methods, our segmentation method can identify the vortex area more accurately, while the time consumption is reduced more than 50%; our classification method can reduce the time consumption by more than 90% while maintaining the same classification accuracy level.

cs.CV↗

arXiv2018

Bias Correction For Paid Search In Media Mix Modeling

Evaluating the return on ad spend (ROAS), the causal effect of advertising on sales, is critical to advertisers for understanding the performance of their existing marketing strategy as well as how to improve and optimize it. Media Mix Modeling (MMM) has been used as a convenient analytical tool to address the problem using observational data. However it is well recognized that MMM suffers from various fundamental challenges: data collection, model specification and selection bias due to ad targeting, among others \citep{chan2017,wolfe2016}. In this paper, we study the challenge associated with measuring the impact of search ads in MMM, namely the selection bias due to ad targeting. Using causal diagrams of the search ad environment, we derive a statistically principled method for bias correction based on the \textit{back-door} criterion \citep{pearl2013causality}. We use case studies to show that the method provides promising results by comparison with results from randomized experiments. We also report a more complex case study where the advertiser had spent on more than a dozen media channels but results from a randomized experiment are not available. Both our theory and empirical studies suggest that in some common, practical scenarios, one may be able to obtain an approximately unbiased estimate of search ad ROAS.

stat.AP↗

arXiv2017

Efficient Aerosol Retrieval for Multi-angle Imaging SpectroRadiometer (MISR): A Bayesian Approach

Recent research in Aerosol Optical Depth (AOD) retrieval algorithms for Multi-angle Imaging SpectroRadiometer (MISR) proposed a hierarchical Bayesian model. However the inference algorithm used in their work was Markov Chain Monte Carlo (MCMC), which was reported prohibitively slow. The poor speed of MCMC dramatically limited the production feasibility of the Bayesian framework if large scale (e.g. global scale) of aerosol retrieval is desired. In this paper, we present an alternative optimization method to mitigate the speed problem. In particular we adopt Maximize a Posteriori (MAP) approach, and apply a gradient-free "hill-climbing" algorithm: the coordinate-wise stochastic-search. Our method has shown to be much (about 100 times) faster than MCMC, easier to converge, and insensitive to hyper parameters. To further scale our approach, we parallelized our method using Apache Spark, which achieves linear speed-up w.r.t number of CPU cores up to 16. Due to these efforts, we are able to retrieve AOD at much finer resolution (1.1km) with a tiny fraction of time consumption compared with existing methods. During our research, we find that in low AOD levels, the Bayesian network tends to produce overestimated retrievals. We also find that high absorbing aerosol types are retrieved at the same time. This is likely caused by the Dirichlet prior for aerosol types, as it is shown to encourage selecting absorbing types in practice. After changing Dirichlet to uniform, the AOD retrievals show excellent agreement with ground measurement in all levels.

stat.AP↗

arXiv2016

Numerical Ranges of the product of Operators

We study containment regions of the numerical range of the product of operators $A$ and $B$ such that $W(A)$ and $W(B)$ are line segments. It is shown that the containment region is equal to the convex hull of elliptical disks determined by the spectrum of $AB$, and conditions on $A$ and $B$ for the set equality holding are obtained. The results cover the case when $A$ and $B$ are self-adjoint operators extending the previous results on the numerical range of the product of two orthogonal projections.

math.FA↗

arXiv2012

A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data

Atmospheric aerosols can cause serious damage to human health and life expectancy. Using the radiances observed by NASA's Multi-angle Imaging SpectroRadiometer (MISR), the current MISR operational algorithm retrieves Aerosol Optical Depth (AOD) at a spatial resolution of 17.6 km x 17.6 km. A systematic study of aerosols and their impact on public health, especially in highly-populated urban areas, requires a finer-resolution estimate of the spatial distribution of AOD values. We embed MISR's operational weighted least squares criterion and its forward simulations for AOD retrieval in a likelihood framework and further expand it into a Bayesian hierarchical model to adapt to a finer spatial scale of 4.4 km x 4.4 km. To take advantage of AOD's spatial smoothness, our method borrows strength from data at neighboring pixels by postulating a Gaussian Markov Random Field prior for AOD. Our model considers both AOD and aerosol mixing vectors as continuous variables. The inference of AOD and mixing vectors is carried out using Metropolis-within-Gibbs sampling methods. Retrieval uncertainties are quantified by posterior variabilities. We also implement a parallel MCMC algorithm to reduce computational cost. We assess our retrievals performance using ground-based measurements from the AErosol RObotic NETwork (AERONET), a hand-held sunphotometer and satellite images from Google Earth. Based on case studies in the greater Beijing area, China, we show that a 4.4 km resolution can improve the accuracy and coverage of remotely-sensed aerosol retrievals, as well as our understanding of the spatial and seasonal behaviors of aerosols. This improvement is particularly important during high-AOD events, which often indicate severe air pollution.

stat.AP↗