pwc-archive/papers-with-abstracts huggingface dataset:
Passive Channel Charting: Locating Passive Targets using a UWB Mesh
Authors: Raffael Poeggel, Maximilian Stahlke, Jonas Pirkl, Jonathan Ott, George Yammine, Tobias Feigl, Christopher Mutschler
Date: 2025-05-15 00:00:00

Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisition. The concept of channel charting reduces this effort by modeling and projecting the manifold of \ac{csi} onto a 2D coordinate space. So far, researchers only applied this concept to active radio localization, where a mobile device intentionally and actively emits a specific signal. In this paper, we apply channel charting to passive localization. We use a pedestrian dead reckoning (PDR) system to estimate a target's velocity and derive a distance matrix from it. We then use this matrix to learn a distance-preserving embedding in 2D space, which serves as a fingerprinting model. In our experiments, we deploy six nodes in a fully connected ultra-wideband (UWB) mesh network to show that our method achieves high localization accuracy, with an average error of just 0.24\,m, even when we train and test on different targets.
Rate Region of ISAC for Pinching-Antenna Systems
Authors: Chongjun Ouyang, Zhaolin Wang, Yuanwei Liu, Zhiguo Ding
Date: 2025-05-15 00:00:00
Tasks: ISAC

The Pinching-Antenna SyStem (PASS) reconstructs wireless channels through \emph{pinching beamforming}, wherein the activated positions of pinching antennas along dielectric waveguides are optimized to shape the radiation pattern. The aim of this article is to analyze the performance limits of employing PASS in integrated sensing and communications (ISAC). Specifically, a PASS-assisted ISAC system is considered, where a pinched waveguide is utilized to simultaneously communicate with a user and sense a target. Closed-form expressions for the achievable communication rate (CR) and sensing rate (SR) are derived to characterize the information-theoretic limits of this dual-functional operation. \romannumeral1) For the single-pinch case, closed-form solutions for the optimal pinching antenna location are derived under \emph{sensing-centric (S-C)}, \emph{communications-centric (C-C)}, and \emph{Pareto-optimal} designs. On this basis, the CR-SR trade-off is characterized by deriving the full CR-SR rate region, which is shown to encompass that of conventional fixed-antenna systems. \romannumeral2) For the multiple-pinch case, an antenna location refinement method is applied to obtain the optimal C-C and S-C pinching beamformers. As a further advance, inner and outer bounds on the achievable CR-SR region are derived using an element-wise alternating optimization technique and by invoking Cauchy-Schwarz and Karamata's inequalities, respectively. Numerical results demonstrate that: \romannumeral1) the derived bounds closely approximate the true CR-SR region; and \romannumeral2) PASS can achieve a significantly larger rate region than conventional-antenna systems.
Subspace-Based Super-Resolution Sensing for Bi-Static ISAC with Clock Asynchronism
Authors: Jingbo Zhao, Zhaoming Lu, J. Andrew Zhang, Jiaxi Zhou, Weicai Li, Tao Gu
Date: 2025-05-15 00:00:00
Tasks: ISAC, parameter estimation, Super-Resolution

Bi-static sensing is an attractive configuration for integrated sensing and communications (ISAC) systems; however, clock asynchronism between widely separated transmitters and receivers introduces time-varying time offsets (TO) and phase offsets (PO), posing significant challenges. This paper introduces a signal-subspace-based framework that estimates decoupled angles, delays, and complex gain sequences (CGS)-- the target-reflected signals -- for multiple dynamic target paths. The proposed framework begins with a novel TO alignment algorithm, leveraging signal subspace or covariance, to mitigate TO variations across temporal snapshots, enabling coherent delay-domain analysis. Subsequently, subspace-based methods are developed to compensate for TO residuals and to perform joint angle-delay estimation. Finally, leveraging the high resolution in the joint angle-delay domain, the framework compensates for the PO and estimates the CGS for each target. The framework can be applied to both single-antenna and multi-antenna systems. Extensive simulations and experiments using commercial Wi-Fi devices demonstrate that the proposed framework significantly surpasses existing solutions in parameter estimation accuracy and delay resolution. Notably, it uniquely achieves a super-resolution in the delay domain, with a probability-of-resolution curve tightly approaching that in synchronized systems.
CFARNet: Learning-Based High-Resolution Multi-Target Detection for Rainbow Beam Radar
Authors: Qiushi Liang, Yeyue Cai, Jianhua Mo, Meixia Tao
Date: 2025-05-15 00:00:00
Tasks: Computational Efficiency

Millimeter-wave (mmWave) OFDM radar equipped with rainbow beamforming, enabled by joint phase-time arrays (JPTAs), provides wide-angle coverage and is well-suited for fast real-time target detection and tracking. However, accurate detection of multiple closely spaced targets remains a key challenge for conventional signal processing pipelines, particularly those relying on constant false alarm rate (CFAR) detectors. This paper presents CFARNet, a learning-based processing framework that replaces CFAR with a convolutional neural network (CNN) for peak detection in the angle-Doppler domain. The network predicts target subcarrier indices, which guide angle estimation via a known frequency-angle mapping and enable high-resolution range and velocity estimation using the MUSIC algorithm. Extensive simulations demonstrate that CFARNet significantly outperforms a CFAR+MUSIC baseline, especially under low transmit power and dense multi-target conditions. The proposed method offers superior angular resolution, enhanced robustness in low-SNR scenarios, and improved computational efficiency, highlighting the potential of data-driven approaches for high-resolution mmWave radar sensing.
Range Resolution of Near-field MIMO Sensing
Authors: Marcin Wachowiak, André Bourdoux, Sofie Pollin
Date: 2025-05-15 00:00:00

The radiative near-field and integration of sensing capabilities are seen as two key components of the next generation of wireless communication systems. In this paper, the sensing performance of a narrowband near-field system is investigated for several practical antenna array geometries and configurations, namely SIMO/MISO and MIMO. In the SIMO/MISO configuration, the antenna aperture is exploited only a single time for either transmit or receive signal processing, while the MIMO configuration exploits both TX and RX processing. Analytical derivations, supported by simulations, show that the MIMO processing improves the maximum near-field range and sensing resolution by approximately a factor of 1.4 as compared to single-aperture systems. The value of the improvement factor is consistent for all considered array geometries. Finally, using a quadratic approximation of the array factor, an analytical improvement factor of $\sqrt{2}$ is derived, clarifying the observed improvements and validating the numerical results.
Constrained Multimodal Sensing-Aided Communications: A Dynamic Beamforming Design
Authors: Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb, Markku Juntti
Date: 2025-05-15 00:00:00
Tasks: Position

Using multimodal sensory data can enhance communications systems by reducing the overhead and latency in beam training. However, processing such data incurs high computational complexity, and continuous sensing results in significant power and bandwidth consumption. This gives rise to a tradeoff between the (multimodal) sensing data acquisition rate and communications performance. In this work, we develop a constrained multimodal sensing-aided communications framework where dynamic sensing and beamforming are performed under a sensing budget. Specifically, we formulate an optimization problem that maximizes the average received signal-to-noise ratio (SNR) of user equipment, subject to constraints on the average number of sensing actions and power budget. Using the Saleh-Valenzuela mmWave channel model, we construct the channel primarily based on position information obtained via multimodal sensing. Stricter sensing constraints reduce the availability of position data, leading to degraded channel estimation and thus lower performance. We apply Lyapunov optimization to solve the problem and derive a dynamic sensing and beamforming algorithm. Numerical evaluations on the DeepSense and Raymobtime datasets show that halving sensing times leads to only up to 7.7% loss in average SNR.
Enriched K-Tier Heterogeneous Satellite Networks Model with User Association Policies
Authors: Zhuhang Li, Bodong Shang
Date: 2025-05-15 00:00:00

In the rapid evolution of the non-terrestrial networks (NTNs), satellite communication has emerged as a focal area of research due to its critical role in enabling seamless global connectivity. In this paper, we investigate two representative user association policies (UAPs) for multi-tier heterogeneous satellite networks (HetSatNets), namely the nearest satellite UAP and the maximum signal-to-interference-plus-noise-ratio (max-SINR) satellite UAP, where each tier is characterized by a distinct constellation configuration and transmission pattern. Employing stochastic geometric, we analyze various intermediate system aspects, including the probability of a typical user accessing each satellite tier, the aggregated interference power, and their corresponding Laplace transforms (LTs) under both UAPs. Subsequently, we derive explicit expressions for coverage probability (CP), non-handover probability (NHP), and time delay outage probability (DOP) of the typical user. Furthermore, we propose a novel weighted metric (WM) that integrates CP, NHP, and DOP to explore their trade-offs in the system design. The robustness of the theoretical framework is verified is verified through Monte Carlo simulations calibrated with the actual Starlink constellation, affirming the precision of our analytical approach. The empirical findings underscore an optimal UAP in various HetSatNet scenarios regarding CP, NHP, and DOP..
Implicit Neural Representation of Waveform Measurements in Power Systems Waveform Data Analysis
Authors: Narges Ehsani, Vishwanath Saragadam, Hamed Mohsenian-Rad
Date: 2025-05-14 00:00:00
Tasks: Fault Detection

There is currently a paradigm shift in several power system monitoring applications, such as incipient fault detection and monitoring inverter-based resources, to transition from traditional phasor analytics to more informative waveform analytics. This paper contributes to this transition by developing a novel approach to modeling voltage and current waveform measurements using implicit neural representations (INRs). INRs are continuous function approximators that are recently used in vision and signal processing. The proposed INR models are specifically designed to meet the requirements of waveform analytics in power systems, such as by using sinusoidal activation functions that capture the periodic nature of voltage and current waveforms. We also propose extended models that can efficiently represent correlated waveforms, such as three-phase waveforms and synchro-waveforms. Real-world case studies demonstrate the effectiveness of the proposed INR models in terms of accuracy (<1-2% MSE) and model size (4-6x compression). We also investigate the application of INR models in oscillation monitoring, for single mode oscillations and dual mode modulated oscillations.
THz-Band Near-Field RIS Channel Modeling for Linear Channel Estimation
Authors: Ahmad Dkhan, Simon Tarboush, Hadi Sarieddeen, Ibrahim Abou-Faycal, Tareq Y. Al-Naffouri
Date: 2025-05-14 00:00:00

Reconfigurable intelligent surface (RIS)-aided terahertz (THz)-band communications are promising enablers for future wireless networks. However, array densification at high frequencies introduces significant challenges in accurate channel modeling and estimation, particularly with THz-specific fading, mutual coupling (MC), spatial correlation, and near-field effects. In this work, we model THz outdoor small-scale fading channels using the mixture gamma (MG) distribution, considering absorption losses, spherical wave propagation, MC, and spatial correlation across large base stations and RISs. We derive the distribution of the cascaded RIS-aided channel and investigate linear channel estimation techniques, analyzing the impact of various channel parameters. Numerical results based on precise THz parameters reveal that accounting for spatial correlation, MC, and near-field modeling substantially enhances estimation accuracy, especially in ultra-massive arrays and short-range scenarios. These results underscore the importance of incorporating these effects for precise, physically consistent channel modeling.
FAS-LLM: Large Language Model-Based Channel Prediction for OTFS-Enabled Satellite-FAS Links
Authors: Halvin Yang, Sangarapillai Lambotharan, Mahsa Derakhshani
Date: 2025-05-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Time Series Forecasting

This paper proposes FAS-LLM, a novel large language model (LLM)-based architecture for predicting future channel states in Orthogonal Time Frequency Space (OTFS)-enabled satellite downlinks equipped with fluid antenna systems (FAS). The proposed method introduces a two-stage channel compression strategy combining reference-port selection and separable principal component analysis (PCA) to extract compact, delay-Doppler-aware representations from high-dimensional OTFS channels. These representations are then embedded into a LoRA-adapted LLM, enabling efficient time-series forecasting of channel coefficients. Performance evaluations demonstrate that FAS-LLM outperforms classical baselines including GRU, LSTM, and Transformer models, achieving up to 10 dB normalized mean squared error (NMSE) improvement and threefold root mean squared error (RMSE) reduction across prediction horizons. Furthermore, the predicted channels preserve key physical-layer characteristics, enabling near-optimal performance in ergodic capacity, spectral efficiency, and outage probability across a wide range of signal-to-noise ratios (SNRs). These results highlight the potential of LLM-based forecasting for delay-sensitive and energy-efficient link adaptation in future satellite IoT networks.
Efficient Near-Field Beam Focusing Merging Orthogonal Matching Pursuit and CVX for Large Intelligent Surface Applications
Authors: Jiawang Li
Date: 2025-05-14 00:00:00

In this paper, an efficient near-field beamforming method is proposed to support the large intelligent surfaces (LIS) that are expected to be widely deployed in 6G networks. This approach avoids directly applying convex (CVX) optimization for sparse selection in large-size array matrices, as such methods often lead to excessive computational time due to blind searching to satisfy a series of objective functions. First, based on the objective function, we prioritize a key component and employ the orthogonal matching pursuit (OMP) method to pre-select potential sparse target positions. To ensure focal symmetry, a coordinate mirror symmetry approach is adopted, meaning that selection is performed only in the first quadrant, while the remaining quadrants are determined through mirror symmetry relative to the first quadrant. This significantly reduces computational complexity at an early stage. Next, CVX is applied based on the pre-selected sparse array. Once a predefined threshold is met, a solution is obtained that satisfies the constraints of the beamfocusing. The results demonstrate that, compared with conventional methods, this approach improves efficiency by 15.12 times with 121 elements and 96.73 times with 441 elements. The proposed method demonstrates not only satisfactory performance but also considerable potential as a beam focusing technique for large-scale near-field array systems.
On Properties of Phase-Conjugation Focusing for Large Intelligent Surface Applications -- Part I: Vertical Polarization
Authors: Jiawang Li
Date: 2025-05-14 00:00:00

Large intelligent surface (LIS) is one promising path to leverage 6G performance in sub-10 GHz bands. This two-part paper explores the properties of phase-conjugation focusing for a simplified LIS setup with a two-dimensional (2D) circular antenna array and a user antenna located within the array aperture in the same plane. In Part I of this article, we assume vertical polarization for all antenna elements, whereas Part II assumes horizontal polarization. In Part I, we focus on the effect of array radius on the peak gain, 3 dB focusing width, and sidelobes for two types of circular arrays. The numerical results show that the gain minimum is located at the array center. The peak gain varies by less than 0.5 dB for focal points located within 2 from the array center. Similarly, the focal width and sidelobe level are also stable within this region, irrespective of array radius. From 2 from the center to the array edge, the closer proximity of the focal points to some array elements than other elements results in more drastic changes in these NFF properties. Finally, full-wave simulation using Ansys HFSS is used to partially validate the numerical results.
On Properties of Phase-Conjugation Focusing for Large Intelligent Surface Applications -- Part II: Horizontal Polarization
Authors: Jiawang Li
Date: 2025-05-14 00:00:00

Near-field focusing (NFF) forms the basis for several applications of large intelligent surface (LIS) in sub-10 GHz bands, including wireless communications, wireless power transfer, positioning, and sensing. In this two-part paper, Part I analyzed the properties of phase conjugation NFF for vertically polarized antennas, in a circular array configuration. In Part II of this article, we continue to study phase conjugation NFF for circular arrays, but for horizontally polarized antennas. We investigate the focusing characteristics of a circular array in two distinct configurations. The numerical results show that the first configuration where all the antenna elements (including the user antenna) are aligned offers significant better performance in terms of peak gain, 3 dB focal width and sidelobe level, relative to the second configuration where the broadside of the elements faces the array center. This result points to the beneficial use of orthogonally oriented horizontally polarized antenna at the user to allow for flexible polarization alignment with the fixed array orientation. In addition, the vertical polarized circular array of Part I may be merged with the first configuration of Part II to provide optimal NFF to a randomly oriented user equipment with a polarization reconfigurable tripole antenna.
Wearable Tracking of Eye and Body Movements During Breaching Training: Towards Real-Time Blast Injury Monitoring
Authors: Jeremy P. Kemmerer, James R. Williamson, Joseph Kim, Elizabeth Halford, Hrishikesh M. Rao, Christopher J. Smalt
Date: 2025-05-14 00:00:00

Repeated exposure to blast overpressure in occupational settings has been associated with changes in cognitive and psychological health, as well as deficits in neurosensory subsystems. In this work, we describe a wearable system to simultaneously monitor physiology and blast exposure levels and demonstrate how this system can identify individualized exposure levels corresponding to acute physiological response to blast exposure. Machine learning was used to develop a dose-response model that fused multiple physiological measures (electrooculuography, gait, and balance) into a single risk score by predicting the level of blast exposure on held-out subjects (Fused model, R = 0.60). We found that blast events with peak pressure levels as low as 0.25 psi could be related to physiological changes and hence may contribute to blast injury. We also identified an individual subject with deteriorating reaction time scores that consistently showed a rapid and anomalous change in physiology-based risk scores after exposure to low-level blast events. Our results suggest that the wearable approach to blast monitoring is viable in weapons training environments as a complement to more direct but sparsely administered brain health assessments, potentially viable in austere environments, and that fusing multiple physiological signals can improve sensitivity.
Empirical Study on Near-Field and Spatial Non-Stationarity Modeling for THz XL-MIMO Channel in Indoor Scenario
Authors: Huixin Xu, Jianhua Zhang, Pan Tang, Hongbo Xing, Chong Han, Lei Tian, Qixing Wang, Guangyi Liu
Date: 2025-05-14 00:00:00

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.
Optimum and Adaptive Complex-Valued Bilinear Filters
Authors: Bernhard Plaimer, Matthias Wagner, Oliver Lang, Mario Huemer
Date: 2025-05-14 00:00:00

The identification of nonlinear systems is a frequent task in digital signal processing. Such nonlinear systems may be grouped into many sub-classes, whereby numerous nonlinear real-world systems can be approximated as bilinear (BL) models. Therefore, various optimum and adaptive BL filters have been introduced in recent years. Further, in many applications such as communications and radar, complex-valued (CV) BL systems in combination with CV signals may occur. Hence, in this work, we investigate the extension of real-valued (RV) BL filters to CV BL filters. First, we derive CV BL filters by applying two or four RV BL filters, and compare them with respect to their computational complexity and performance. Second, we introduce novel fully CV BL filters, such as the CV BL Wiener filter (WF), the CV BL least squares (LS) filter, the CV BL least mean squares (LMS) filter, the CV BL normalized LMS (NLMS) filter, and the CV BL recursive least squares (RLS) filter. Finally, these filters are applied to identify multiple-input-single-output (MISO) systems and Hammerstein models.