pwc-archive/papers-with-abstracts huggingface dataset:
Set-Based Position Ambiguity Reduction Method for Zonotope Shadow Matching in Urban Areas Using Estimated Multipath Errors
Authors: Sanghyun Kim, Jiwon Seo
Date: 2025-02-16 00:00:00
Tasks: Position

In urban areas, the quality of global navigation satellite system (GNSS) signals deteriorates, leading to reduced positioning accuracy. To address this issue, 3D-mapping-aided (3DMA) techniques, such as shadow matching and zonotope shadow matching (ZSM), have been proposed. However, these methods can introduce a problem known as multi-modal position ambiguity, making it challenging to select the exact mode in which the receiver is located. Accurately selecting the correct mode is essential for improving positioning accuracy. A previous study proposed a method that uses satellite-pseudorange consistency (SPC), calculated from pseudorange measurements, to select the mode containing the receiver. This method achieved a mode selection accuracy of approximately 78%. To further enhance accuracy, the study utilized pseudorange measurements collected at multiple timesteps from a fixed location and a trained line-of-sight (LOS) classifier. However, in practice, collecting data at multiple timesteps from the same location in dynamic environments is challenging. Moreover, the performance of the trained LOS classifier heavily depends on the surrounding environment, leading to low reliability. In this study, we propose a method that estimates and corrects multipath errors based on the mode distribution obtained from the output of ZSM and extract an enhanced SPC using the corrected pseudorange measurements. This enables high mode selection accuracy using only single-timestep pseudorange measurements, without requiring a trained LOS classifier.
Non-negative tensor factorization-based dependence map analysis for local damage detection in presence of non-Gaussian noise
Authors: Anna Michalak, Justyna Hebda-Sobkowicz, Anil Kumar, Radoslaw Zimroz, Rafal Zdunek, Agnieszka Wylomanska
Date: 2025-02-16 00:00:00

The time-frequency map (TFM) is frequently used in condition monitoring, necessitating further processing to select an informative frequency band (IFB) or directly detect damage. However, selecting an IFB is challenging due to the complexity of spectral structures, non-Gaussian disturbances, and overlapping fault signatures in vibration signals. Additionally, dynamic operating conditions and low signal-to-noise ratio further complicate the identification of relevant features that indicate damage. To solve this problem, the present work proposes a novel method for informative band selection and local damage detection in rolling element bearings, utilizing non-negative tensor factorization (NTF)-based dependence map analysis. The recently introduced concept of the dependence map is leveraged, with a set of these maps being factorized to separate informative components from non-informative ones. Dependence maps provide valuable information on the auto-similarity of spectral content, while NTF, a powerful tool commonly used in image processing for feature extraction, enhances this process. The combination of these methods allows for the extraction of IFBs, forming the basis for local damage detection. The effectiveness of the proposed method has been validated using both synthetic and real vibration signals corrupted with non-Gaussian disturbances.
Robust correlation measures for informative frequency band selection in heavy-tailed vibration signal
Authors: Justyna Hebda-Sobkowicz, Radosław Zimroz, Anil Kumar, Agnieszka Wyłomanska
Date: 2025-02-16 00:00:00

Vibration signals are commonly used to detect local damage in rotating machinery. However, raw signals are often noisy, particularly in crusher machines, where the technological process (falling pieces of rock) generates random impulses that complicate detection. To address this, signal pre-filtering (extracting the informative frequency band from noise-affected signals) is necessary. This paper proposes an algorithm for processing vibration signals from a bearing used in an ore crusher. Selecting informative frequency bands (IFBs) in the presence of impulsive noise is notably challenging. The approach employs correlation maps to detect cyclic behavior within specific frequency bands in the time-frequency domain (spectrogram), enabling the identification of IFBs. Robust correlation measures and median filtering are applied to enhance the correlation maps and improve the final IFB selection. Signal segmentation and the use of averaged results for IFB selection are also highlighted. The proposed trimmed and quadrant correlations are compablack with the Pearson and Kendall correlations using simulated signal, real vibration signal from crusher in mining industry and acoustic signal measublack on the test rig. Furthermore, the results of real vibration analyses are compablack with established IFB selectors, including the spectral kurtosis, the alpha selector and the conditional variance-based selector.
Unveiling the Power of Complex-Valued Transformers in Wireless Communications
Authors: Yang Leng, Qingfeng Lin, Long-Yin Yung, Jingreng Lei, Yang Li, Yik-Chung Wu
Date: 2025-02-16 00:00:00
Tasks: Action Detection, Activity Detection

Utilizing complex-valued neural networks (CVNNs) in wireless communication tasks has received growing attention for their ability to provide natural and effective representation of complex-valued signals and data. However, existing studies typically employ complex-valued versions of simple neural network architectures. Not only they merely scratch the surface of the extensive range of modern deep learning techniques, theoretical understanding of the superior performance of CVNNs is missing. To this end, this paper aims to fill both the theoretical and practice gap of employing CVNNs in wireless communications. In particular, we provide a comprehensive description on the various operations in CVNNs and theoretically prove that the CVNN requires fewer layers than the real-valued counterpart to achieve a given approximation error of a continuous function. Furthermore, to advance CVNNs in the field of wireless communications, this paper focuses on the transformer model, which represents a more sophisticated deep learning architecture and has been shown to have excellent performance in wireless communications but only in its real-valued form. In this aspect, we propose a fundamental paradigm of complex-valued transformers for wireless communications. Leveraging this structure, we develop customized complex-valued transformers for three representative applications in wireless communications: channel estimation, user activity detection, and precoding design. These applications utilize transformers with varying levels of sophistication and span a variety of tasks, ranging from regression to classification, supervised to unsupervised learning, and specific module design to end-to-end design. Experimental results demonstrate the superior performance of the complex-valued transformers for the above three applications compared to other traditional real-valued neural network-based methods.
Improved VMD Based Remote Heartbeat Estimation Utilizing 60GHz mmWave Radar
Authors: Boyuan Gu, Yanhui Yang, Siyu You, Haiyang Sun, Jiahui Sun, Shisheng Guo
Date: 2025-02-16 00:00:00

This study introduces an improved VMD based signal decomposition methodology for non-contact heartbeat estimation using millimeterwave (mmWave) radar. Specifically, we first analyze the signal model of the mmWave radar system. The Variational Mode Decomposition (VMD) integrated with the Newton-Raphson-based optimizer (NRBO) algorithm are sequentially utilized for cardiac mechanic signal (CMS) reconstruction. The estimation accuracy is enhanced by adaptively optimizing the VMD parameters including intrinsic mode functions (IMFs) and penalty factor. Eventually, the experimental results of 18 subjects validate the effectiveness of the proposed method by comparing with three commonly used baselines.
DT4ECG: A Dual-Task Learning Framework for ECG-Based Human Identity Recognition and Human Activity Detection
Authors: Siyu You, Boyuan Gu, Yanhui Yang, Shiyu Yu, Shisheng Guo
Date: 2025-02-16 00:00:00
Tasks: Action Detection, Activity Detection, Multi-Task Learning

This article introduces DT4ECG, an innovative dual-task learning framework for Electrocardiogram (ECG)-based human identity recognition and activity detection. The framework employs a robust one-dimensional convolutional neural network (1D-CNN) backbone integrated with residual blocks to extract discriminative ECG features. To enhance feature representation, we propose a novel Sequence Channel Attention (SCA) mechanism, which combines channel-wise and sequential context attention to prioritize informative features across both temporal and channel dimensions. Furthermore, to address gradient imbalance in multi-task learning, we integrate GradNorm, a technique that dynamically adjusts loss weights based on gradient magnitudes, ensuring balanced training across tasks. Experimental results demonstrate the superior performance of our model, achieving accuracy rates of 99.12% in ID classification and 90.11% in activity classification. These findings underscore the potential of the DT4ECG framework in enhancing security and user experience across various applications such as fitness monitoring and personalized healthcare, thereby presenting a transformative approach to integrating ECG-based biometrics in everyday technologies.
Robust Multidimensional Graph Neural Networks for Signal Processing in Wireless Communications with Edge-Graph Information Bottleneck
Authors: Ziheng Liu, Jiayi Zhang, Yiyang Zhu, Enyu Shi, Bo Ai
Date: 2025-02-15 00:00:00

Signal processing is crucial for satisfying the high data rate requirements of future sixth-generation (6G) wireless networks. However, the rapid growth of wireless networks has brought about massive data traffic, which hinders the application of traditional optimization theory-based algorithms. Meanwhile, traditional graph neural networks (GNNs) focus on compressing inputs onto vertices to update representations, which often leads to their inability to effectively distinguish input features and severely weakens performance. In this context, designing efficient signal processing frameworks becomes imperative. Moreover, actual scenarios are susceptible to multipath interference and noise, resulting in specific differences between the received and actual information. To address these challenges, this paper incorporates multidimensional graph neural networks (MDGNNs) with edge-graph information bottleneck (EGIB) to design a robust framework for signal processing. Specifically, MDGNNs utilize hyper-edges instead of vertices to update representations to avoid indistinguishable features and reduce information loss, while EGIB encourages providing minimal sufficient information about outputs to avoid aggregation of irrelevant information. We numerically demonstrate that compared with existing frameworks, the proposed frameworks achieve excellent performance in terms of spectrum efficiency (SE) and network overhead under multiple signal processing tasks. Remarkably, as the interference noise increases, the SE performance of the proposed frameworks gradually stabilizes. This reveals the proposed frameworks have excellent robustness in interference prone environments, especially in wireless policies related to channel matrices.
Blind Massive MIMO for Dense IoT Networks
Authors: Jeongjae Lee, SongNam Hong
Date: 2025-02-15 00:00:00

In this paper, we investigate the downlink communication challenges in heavy-load Internet-of-Things (IoT) networks supported by frequency-division-duplexing (FDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. The excessive overhead required for obtaining channel state information at the transmitter (CSIT) is essential to achieve high spectral efficiency through conventional massive MIMO techniques; however, it hinders the deployment of ultra-reliable low-latency communications (URLLC) and leads to significant energy expenditure, particularly in dense IoT networks. To address this challenge, we propose an innovative CSIT-Free MIMO precoding method, referred to as CIRculant information Classification via Linear Estimation (CIRCLE). Our major contribution is the design of a CSIT-independent (or deterministic) precoding, which is constructed by leveraging the circulant permutation of the discrete Fourier transform (DFT) matrix. This design enables interference-free signal combining at the IoT devices. Through theoretical analysis and simulations, we verify the effectiveness of the proposed CIRCLE method.
PainDECOG: Machine Learning-Based Identification of Pain Biomarkers from sEEG Signals
Authors: Sidharth Sidharth, Vishwas Sathish, Shweta Bansal, Samantha Sun, Timmy Pham, Kurt Weaver, Rajesh P. N. Rao, Jeffrey Herron
Date: 2025-02-15 00:00:00
Tasks: Feature Engineering, Management

This study presents a systematic machine-learning approach for classifying acute pain from raw electrophysiological signals. We address binary and ternary classification tasks, leveraging Power-In-Band (PIB) and signal coherence as distinguishing features. Our method evaluates the effectiveness of traditional machine learning algorithms on a manually curated electrophysiological dataset obtained from intracranial electroencephalography (iEEG), offering valuable insights into model performance for pain detection. Furthermore, we identify critical electrode pairings associated with acute pain, providing a clearer understanding of the neural markers that differentiate pain states. This work highlights the potential of targeted feature engineering in advancing pain classification, setting the stage for future enhancements in real-time and personalized pain assessment tools. Additionally, these findings have promising applications in neuromodulation and Deep Brain Stimulation (DBS), where adaptive and closed-loop systems could leverage identified pain markers to modulate pain-related brain regions more precisely, offering improved therapeutic options for chronic pain management
Multi-view 3D surface reconstruction from SAR images by inverse rendering
Authors: Emile Barbier--Renard, Florence Tupin, Nicolas Trouvé, Loïc Denis
Date: 2025-02-14 00:00:00
Tasks: 3D Reconstruction, Inverse Rendering, Surface Reconstruction

3D reconstruction of a scene from Synthetic Aperture Radar (SAR) images mainly relies on interferometric measurements, which involve strict constraints on the acquisition process. These last years, progress in deep learning has significantly advanced 3D reconstruction from multiple views in optical imaging, mainly through reconstruction-by-synthesis approaches pioneered by Neural Radiance Fields. In this paper, we propose a new inverse rendering method for 3D reconstruction from unconstrained SAR images, drawing inspiration from optical approaches. First, we introduce a new simplified differentiable SAR rendering model, able to synthesize images from a digital elevation model and a radar backscattering coefficients map. Then, we introduce a coarse-to-fine strategy to train a Multi-Layer Perceptron (MLP) to fit the height and appearance of a given radar scene from a few SAR views. Finally, we demonstrate the surface reconstruction capabilities of our method on synthetic SAR images produced by ONERA's physically-based EMPRISE simulator. Our method showcases the potential of exploiting geometric disparities in SAR images and paves the way for multi-sensor data fusion.
Physics-Informed Generative Modeling of Wireless Channels
Authors: Benedikt Böck, Andreas Oeldemann, Timo Mayer, Francesco Rossetto, Wolfgang Utschick
Date: 2025-02-14 00:00:00

Learning the site-specific distribution of the wireless channel within a particular environment of interest is essential to exploit the full potential of machine learning (ML) for wireless communications and radar applications. Generative modeling offers a promising framework to address this problem. However, existing approaches pose unresolved challenges, including the need for high-quality training data, limited generalizability, and a lack of physical interpretability. To address these issues, we combine the physics-related compressibility of wireless channels with generative modeling, in particular, sparse Bayesian generative modeling (SBGM), to learn the distribution of the underlying physical channel parameters. By leveraging the sparsity-inducing characteristics of SBGM, our methods can learn from compressed observations received by an access point (AP) during default online operation. Moreover, they are physically interpretable and generalize over system configurations without requiring retraining.
Near-Field Localization with Physics-Compliant Electromagnetic Model: Algorithms and Model Mismatch Analysis
Authors: Alexandr M. Kuzminskiy, Ahmed Elzanaty, Gabriele Gradoni, Fan Wang, Rahim Tafazolli
Date: 2025-02-14 00:00:00
Tasks: model, Model Selection

Accurate signal localization is critical for Internet of Things applications, but precise propagation models are often unavailable due to uncontrollable factors. Simplified models such as planar and spherical wavefront approximations are widely used but can cause model mismatches that reduce accuracy. To address this, we propose an expected likelihood ratio framework for model mismatch analysis and online model selection without requiring knowledge of the true propagation model. The framework leverages the scenario independent distribution of the likelihood ratio of the actual covariance matrix, enabling the detection of mismatches and outliers by comparing given models to a predefined distribution. When an accurate electromagnetic model is unavailable, the robustness of the framework is analyzed using data generated from a precise electromagnetic model and simplified models within positioning algorithms. Validation in direct localization and reconfigurable intelligent surface assisted scenarios demonstrates the ability to improve localization accuracy and reliably detect model mismatches in diverse Internet of Things environments.
Low-Complexity On-Grid Channel Estimation for Partially-Connected Hybrid XL-MIMO
Authors: Sunho Kim, Wan Choi
Date: 2025-02-14 00:00:00
Tasks: Quantization

This paper addresses the challenge of channel estimation in extremely large-scale multiple-input multiple-output (XL-MIMO) systems, pivotal for the advancement of 6G communications. XL-MIMO systems, characterized by their vast antenna arrays, necessitate accurate channel state information (CSI) to leverage high spatial multiplexing and beamforming gains. However, conventional channel estimation methods for near-field XL-MIMO encounter significant computational complexity due to the exceedingly high parameter quantization levels needed for estimating the parametric near-field channel. To address this, we propose a low-complexity two-stage on-grid channel estimation algorithm designed for near-field XL-MIMO systems. The first stage focuses on estimating the LoS channel component while treating the NLoS paths as interference. This estimation is accomplished through an alternating subarray-wise array gain maximization (ASAGM) approach based on the piecewise outer product model (SOPM). In the second stage, we estimate the NLoS channel component by utilizing the sensing matrix refinement-based orthogonal matching pursuit (SMR-OMP) algorithm. This approach helps reduce the high computational complexity associated with large-dimensional joint sensing matrices. Simulation results demonstrate the effectiveness of our proposed low-complexity method, showcasing its significant superiority over existing near-field XL-MIMO channel estimation techniques, particularly in intermediate and high SNR regimes, and in practical scenarios involving arbitrary array placements.
Port-LLM: A Port Prediction Method for Fluid Antenna based on Large Language Models
Authors: Yali Zhang, Haifan Yin, Weidong Li, Emil Bjornson, Merouane Debbah
Date: 2025-02-14 00:00:00
Tasks: Prediction

The objective of this study is to address the mobility challenges faced by User Equipment (UE) through the implementation of fluid antenna (FA) on the UE side. This approach aims to maintain the time-varying channel in a relatively stable state by strategically relocating the FA to an appropriate port. To the best of our knowledge, this paper introduces, for the first time, the application of large language models (LLMs) in the prediction of FA ports, presenting a novel model termed Port-LLM. The proposed method consists of two primary steps for predicting the moving port of the FA: the first involves utilizing the channel tables that encompass historical channel state information from all movable ports of the FA to forecast the channel tables for subsequent time periods; the second step entails selecting the port of the FA for the forthcoming time based on the predicted channel tables and the known reference channels that require alignment. To enhance the learning capabilities of the LLM model in the context of FA port prediction, we incorporate the Low-Rank Adaptation (LoRA) fine-tuning technology. Furthermore, during the model training phase, we implement the warm-up-aided cosine learning rate (LR) technique to augment the accuracy of the predictions. The simulation results show that our model exhibits strong generalization ability and robustness under different numbers of base station antennas and medium-to-high mobility speeds of UE. In comparison to existing FA port calculation methods, the performance of the port predicted by our model demonstrates superior efficacy. Additionally, our model exhibits lower prediction costs and faster prediction and reasoning speeds.
Compressive Sensing Empirical Wavelet Transform for Frequency-Banded Power Measurement Considering Interharmonics
Authors: Jian Liu, Wei Zhao, Shisong Li
Date: 2025-02-14 00:00:00
Tasks: Compressive Sensing

Power measurement algorithms based on Fourier transform are susceptible to errors caused by interharmonics, while wavelet transform algorithms are particularly sensitive to even harmonics due to band decomposition effects. The empirical wavelet transform (EWT) has been demonstrated to improve measurement accuracy by effectively partitioning transition bands. However, for detecting interharmonic components, the limitation of the observation time window restricts spectral resolution, thereby limiting measurement accuracy. To address this challenge, this paper proposes a Compressive Sensing Empirical Wavelet Transform (CSEWT). The approach aims to enhance frequency resolution by integrating compressive sensing with the EWT, allowing precise identification of components across different frequency bands. This enables accurate determination of the power associated with the fundamental frequency, harmonics, and interharmonics. Test results indicate that the proposed CSEWT method can significantly improve the precision of individual frequency component measurements, even under dynamic and noisy conditions.
Joint Beamforming and Antenna Position Optimization for Multi-user Position-Reconfigurable Full-Duplex MIMO Systems
Authors: Chengjie Zhao, Tho Le-Ngoc
Date: 2025-02-14 00:00:00
Tasks: Position

Fluid antenna system (FAS) or movable antenna system (MAS) is an emerging antenna design technique that can fully exploit the wireless channel spatial variation and consequently improve the communication performance, both in terms of achievable rate and hardware cost, via relocating the positions of antennas in a confined region. Full-duplex (FD) communication enables simultaneous transmission and reception over the same time and frequency resource block, which theoretically double the spectral efficiency. The integration of FAS/MAS and FD communication is expected to greatly improve the system capacity and save the spectral and hardware resources.