pwc-archive/papers-with-abstracts huggingface dataset:
Toeplitz-Hermitian ADMM-Net for DoA Estimation
Authors: Youval Klioui
Date: 2025-02-19 00:00:00
Tasks: Direction of Arrival Estimation

This paper presents Toeplitz-Hermitian ADMM-Net (THADMM-Net), a deep neural network obtained by deep unfolding the alternating direction method of multipliers (ADMM) algorithm for solving the least absolute shrinkage thresholding operator problem in the context of direction of arrival estimation. By imposing both a Toeplitz-Hermitian as well as positve semi-definite constraint on the learnable matrices, the total parameter count required per layer is reduced from $N^2$ to approximately $N$ where $N$ is the length of the dictionary used in the sparse recovery problem. Numerical simulations show that with a lower parameter count and depth, THADMM-Net outperforms Toeplitz-Lista with respect to the normalized mean-squared error, the detection rate, as well as the root mean-squared error over a signal-to-noise ratio between 0 dB and 35 dB.
Intrinsic Cramér-Rao Bound based 6D Localization and Tracking for 5G/6G Systems
Authors: Xueting Xu, Hui Chen, Shengqiang Shen, Hyowon Kim, Xu Fang, Ao Peng, Fan Jiang, Henk Wymeersch
Date: 2025-02-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

Localization and tracking are critical components of integrated sensing and communication (ISAC) systems, enhancing resource management, beamforming accuracy, and overall system reliability through precise sensing. Due to the high path loss of the high-frequency systems, antenna arrays are required at the transmitter and receiver sides for beamforming gain. However, beam misalignment may occur, which requires accurate tracking of the six-dimensional (6D) state, namely, 3D position and 3D orientation. In this work, we first address the challenge that the rotation matrix, being part of the Lie group rather than Euclidean space, necessitates the derivation of the ICRB for an intrinsic performance benchmark. Then, leveraging the derived ICRB, we develop two filters-one utilizing pose fusion and the other employing error-state Kalman filter to estimate the UE's 6D state for different computational resource consumption and accuracy requirements. Simulation results validate the ICRB and assess the performance of the proposed filters, demonstrating their effectiveness and improved accuracy in 6D state tracking.
Millimeter-Wave ISAC Testbed Using Programmable Digital Coding Dynamic Metasurface Antenna: Practical Design and Implementation
Authors: Abdul Jabbar, Mostafa Elsayed, Jalil Ur-Rehman Kazim, Zhibo Pang, Julien Le Kernec, Muhammad Imran, Hadi Larijani, Masood Ur-Rehman, Qammer Abbasi, Muhammad Usman
Date: 2025-02-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

Dynamic Metasurface Antennas (DMAs) are transforming reconfigurable antenna technology by enabling energy-efficient, cost-effective beamforming through programmable meta-elements, eliminating the need for traditional phase shifters and delay lines. This breakthrough technology is emerging to revolutionize beamforming for next-generation wireless communication and sensing networks. In this paper, we present the design and real-world implementation of a DMA-assisted wireless communication platform operating in the license-free 60 GHz millimeter-wave (mmWave) band. Our system employs high-speed binary-coded sequences generated via a field-programmable gate array (FPGA), enabling real-time beam steering for spatial multiplexing and independent data transmission. A proof-of-concept experiment successfully demonstrates high-definition quadrature phase-shift keying (QPSK) modulated video transmission at 62 GHz. Furthermore, leveraging the DMA's multi-beam capability, we simultaneously transmit video to two spatially separated receivers, achieving accurate demodulation. We envision the proposed mmWave testbed as a platform for enabling the seamless integration of sensing and communication by allowing video transmission to be replaced with sensing data or utilizing an auxiliary wireless channel to transmit sensing information to multiple receivers. This synergy paves the way for advancing integrated sensing and communication (ISAC) in beyond-5G and 6G networks. Additionally, our testbed demonstrates potential for real-world use cases, including mmWave backhaul links and massive multiple-input multiple-output (MIMO) mmWave base stations.
Binary Deterministic Sensing Matrix Construction Using Manifold Optimization
Authors: Mohamad Mahdi Mohades, Hossein Mohades, S. Fatemeh Zamanian
Date: 2025-02-19 00:00:00

Binary deterministic sensing matrices are highly desirable for sampling sparse signals, as they require only a small number of sum-operations to generate the measurement vector. Furthermore, sparse sensing matrices enable the use of lowcomplexity algorithms for signal reconstruction. In this paper, we propose a method to construct low-density binary deterministic sensing matrices by formulating a manifold-based optimization problem on the statistical manifold. The proposed matrices can be of arbitrary sizes, providing a significant advantage over existing constructions. We also prove the convergence of the proposed algorithm. The proposed binary sensing matrices feature low coherence and constant column weight. Simulation results demonstrate that our method outperforms existing binary sensing matrices in terms of reconstruction percentage and signal to noise ratio (SNR).
Research on the Offshore Marine Communication Environment Based on Satellite Remote Sensing Data
Authors: Hanyue Ni, Jingsong Yang, Lin Ren, Xiaohui Li, Changming Dong, Wen Chen
Date: 2025-02-19 00:00:00
Tasks: Friction

Air-sea interface fluxes significantly impact the reliability and efficiency of maritime communication. Compared to sparse in-situ ocean observations, satellite remote sensing data offers broader coverage and extended temporal span. This study utilizes COARE V3.5 algorithm to calculate momentum flux, sensible heat flux, and latent heat flux at the air-sea interface, based on satellite synthetic aperture radar (SAR) wind speed data, reanalysis data, and buoy measurements, combined with neural network methods. Findings indicate that SAR wind speed data corrected via neural networks show improved consistency with buoy-measured wind speeds in flux calculations. Specifically, the bias in friction velocity decreased from -0.03 m/s to 0.01 m/s, wind stress bias from -0.03 N/m^2 to 0.00 N/m^2, drag coefficient bias from -0.29 to -0.21, latent heat flux bias from -8.32 W/m^2 to 5.41 W/m^2, and sensible heat flux bias from 0.67 W/m^2 to 0.06 W/m^2. Results suggest that the neural network-corrected SAR wind speed data can provide more reliable environmental data for maritime communication.
Uplink Coordinated Pilot Design for 1-bit Massive MIMO in Correlated Channel
Authors: Hyeongtak Yun, Juntaek Han, Kaiming Shen, Jeonghun Park
Date: 2025-02-19 00:00:00

In this paper, we propose a coordinated pilot design method to minimize the channel estimation mean squared error (MSE) in 1-bit analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO). Under the assumption that the well-known Bussgang linear minimum mean square error (BLMMSE) estimator is used for channel estimation, we first observe that the resulting MSE leads to an intractable optimization problem, as it involves the arcsin function and a complex multiple matrix ratio form. To resolve this, we derive the approximate MSE by assuming the low signal-to-noise ratio (SNR) regime, by which we develop an efficient coordinated pilot design based on a fractional programming technique. The proposed pilot design is distinguishable from the existing work in that it is applicable in general system environments, including correlated channel and multi-cell environments. We demonstrate that the proposed method outperforms the channel estimation accuracy performance compared to the conventional approaches.
Artificial Intelligence-derived Vascular Age from Photoplethysmography: A Novel Digital Biomarker for Cardiovascular Health
Authors: Guangkun Nie, Qinghao Zhao, Gongzheng Tang, Yaxin Li, Shenda Hong
Date: 2025-02-18 00:00:00
Tasks: Photoplethysmography (PPG)

With the increasing availability of wearable devices, photoplethysmography (PPG) has emerged as a promising non-invasive tool for monitoring human hemodynamics. We propose a deep learning framework to estimate vascular age (AI-vascular age) from PPG signals, incorporating a distribution-aware loss to address biases caused by imbalanced data. The model was developed using data from the UK Biobank (UKB), with 98,672 participants in the development cohort and 113,559 participants (144,683 data pairs) for clinical evaluation. After adjusting for key confounders, individuals with a vascular age gap (AI-vascular age minus calendar age) exceeding 9 years had a significantly higher risk of major adverse cardiovascular and cerebrovascular events (MACCE) (HR = 2.37, p < 0.005) and secondary outcomes, including diabetes (HR = 2.69, p < 0.005), hypertension (HR = 2.88, p < 0.005), coronary heart disease (HR = 2.20, p < 0.005), heart failure (HR = 2.15, p < 0.005), myocardial infarction (HR = 2.51, p < 0.005), stroke (HR = 2.55, p < 0.005), and all-cause mortality (HR = 2.51, p < 0.005). Conversely, participants with a vascular age gap below -9 years exhibited a significantly lower incidence of these outcomes. We further evaluated the longitudinal applicability of AI-vascular age using serial PPG data from the UKB, demonstrating its value in risk stratification by leveraging AI-vascular age at two distinct time points to predict future MACCE incidence. External validation was performed on a MIMIC-III-derived cohort (n = 2,343), where each one-year increase in vascular age gap was significantly associated with elevated in-hospital mortality risk (OR = 1.02, p < 0.005). In conclusion, our study establishes AI-vascular age as a novel, non-invasive digital biomarker for cardiovascular health assessment.
Efficient UAV Coverage in Large Convex Quadrilateral Areas with Elliptical Footprints
Authors: Alexander Vavoulas, Konstantinos K. Delibasis, Harilaos G. Sandalidis, George Nousias, Nicholas Vaiopoulos
Date: 2025-02-18 00:00:00

Unmanned Aerial Vehicles (UAVs) have gained significant attention for improving wireless communication, especially in emergencies or as a complement to existing cellular infrastructure. This letter addresses the problem of efficiently covering a large convex quadrilateral using multiple UAVs, where each UAV generates elliptical coverage footprints based on its altitude and antenna tilt. The challenge is approached using circle-packing techniques within a unit square to arrange UAVs in an optimal configuration. Subsequently, a homography transformation is applied to map the unit square onto the quadrilateral area, ensuring that the UAVs' elliptical footprints cover the entire region. Numerical simulations demonstrate the effectiveness of the proposed method, providing insight into coverage density and optimal altitude configurations for different placement scenarios. The results highlight the scalability and potential for improving UAV-based communication systems, focusing on maximizing coverage efficiency in large areas with irregular shapes.
A Bayesian Perspective on Uncertainty Quantification for Estimated Graph Signals
Authors: Lennard Rompelberg, Michael T. Schaub
Date: 2025-02-18 00:00:00
Tasks: Imputation, Uncertainty Quantification

We present a Bayesian perspective on quantifying the uncertainty of graph signals estimated or reconstructed from imperfect observations. We show that many conventional methods of graph signal estimation, reconstruction and imputation, can be reinterpreted as finding the mean of a posterior Gaussian distribution, with a covariance matrix shaped by the graph structure. In this perspective, assumptions of signal smoothness as well as bandlimitedness are naturally expressible as the choice of certain prior distributions; noisy, noise-free or partial observations are expressible in terms of certain likelihood models. In addition to providing a point estimate, as most standard estimation strategies do, our probabilistic framework enables us to characterize the shape of the estimated signal distribution around the estimate in terms of the posterior covariance matrix.
Beyond Diagonal RIS for ISAC Network: Statistical Analysis and Network Parameter Estimation
Authors: Thanh Luan Nguyen, Georges Kaddoum, Bassant Selim, Chadi Assi
Date: 2025-02-18 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

This paper investigates the use of beyond diagonal reconfigurable intelligent surface (BD-RIS) with $N$ elements to advance integrated sensing and communication (ISAC). We address a key gap in the statistical characterizations of the radar signal-to-noise ratio (SNR) and the communication signal-to-interference-plus-noise ratio (SINR) by deriving tractable closed-form cumulative distribution functions (CDFs) for these metrics. Our approach maximizes the radar SNR by jointly configuring radar beamforming and BD-RIS phase shifts. Subsequently, zero-forcing is adopted to mitigate user interference, enhancing the communication SINR. To meet ISAC outage requirements, we propose an analytically-driven successive non-inversion sampling (SNIS) algorithm for estimating network parameters satisfying network outage constraints. Numerical results illustrate the accuracy of the derived CDFs and demonstrate the effectiveness of the proposed SNIS algorithm.
Exact Sum Distribution of alpha-eta-kappa-mu Fading Channels for Statistical Performance Analysis of RRS-Based Wireless Transmission
Authors: Rayees Ahmad Wani, Rajeev Rajagopal, Pranay Bhardwaj, El Mehdi Amhoud, S. M. Zafaruddin
Date: 2025-02-18 00:00:00

Reconfigurable refractive surface (RRS) is an efficient alternative to holographic multiple input multiple outputs (HMIMO) systems that can serve as a transmission unit operating in the signal refraction mode. RRS transmissions experience near-field propagation due to the proximity of the transmission feed and far-field propagation for the user located farther from the transmitting unit. There is limited research on the effect of channel fading on far-field users in RRS-based transmissions. In this paper, we conduct an exact statistical analysis of RRS-based transmission considering alpha-eta-kappa-mu fading model for the far-field user and the near-field effect of transmission feed. First, we show the exact statistical analysis for the RRS transmission over alpha-eta-kappa-mu fading model consisting of multiple infinite-series representations with multivariate Fox-H function. Next, we develop a novel approach to derive the density and distribution functions for the resultant fading channel of the RRS system in terms of multivariate Fox-H functions without involving infinite series approximations for tractable performance analysis. We present the exact outage probability and average bit-error-rate (BER) performance of single-element and multiple-element RRS systems to validate the proposed analysis further. We also obtain the diversity order of the system by analyzing the outage probability at a high signal-to-noise ratio (SNR). Computer simulations demonstrate the relevance of the developed statistical results for RRS-based wireless systems over the generalized fading model for a comprehensive performance evaluation.
One-bit Compressed Sensing using Generative Models
Authors: Swatantra Kafle, Geethu Joseph, Pramod K. Varshney
Date: 2025-02-18 00:00:00
Tasks: compressed sensing

This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pre-trained neural network, learns to map from a low-dimensional latent space to a higher-dimensional set of sparse vectors. This generator is then used to reconstruct sparse vectors from their one-bit measurements by searching over its range. The presented algorithm provides an excellent reconstruction performance because the generative model can learn additional structural information about the signal beyond sparsity. Furthermore, we provide theoretical guarantees on the reconstruction accuracy and sample complexity of the algorithm. Through numerical experiments using three publicly available image datasets, MNIST, Fashion-MNIST, and Omniglot, we demonstrate the superior performance of the algorithm compared to other existing algorithms and show that our algorithm can recover both the amplitude and the direction of the signal from one-bit measurements.
Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks
Authors: Jingzhi Hu, Xin Li, Zhou Su, Jun Luo
Date: 2025-02-18 00:00:00
Tasks: Continual Learning, ISAC

In wireless networks with integrated sensing and communications (ISAC), edge intelligence (EI) is expected to be developed at edge devices (ED) for sensing user activities based on channel state information (CSI). However, due to the CSI being highly specific to users' characteristics, the CSI-activity relationship is notoriously domain dependent, essentially demanding EI to learn sufficient datasets from various domains in order to gain cross-domain sensing capability. This poses a crucial challenge owing to the EDs' limited resources, for which storing datasets across all domains will be a significant burden. In this paper, we propose the EdgeCL framework, enabling the EI to continually learn-then-discard each incoming dataset, while remaining resilient to catastrophic forgetting. We design a transformer-based discriminator for handling sequences of noisy and nonequispaced CSI samples. Besides, we propose a distilled core-set based knowledge retention method with robustness-enhanced optimization to train the discriminator, preserving its performance for previous domains while preventing future forgetting. Experimental evaluations show that EdgeCL achieves 89% of performance compared to cumulative training while consuming only 3% of its memory, mitigating forgetting by 79%.
RadSplatter: Extending 3D Gaussian Splatting to Radio Frequencies for Wireless Radiomap Extrapolation
Authors: Yiheng Wang, Ye Xue, Shutao Zhang, Tsung-Hui Chang
Date: 2025-02-18 00:00:00
Tasks: 3DGS, Autonomous Driving

A radiomap represents the spatial distribution of wireless signal strength, critical for applications like network optimization and autonomous driving. However, constructing radiomap relies on measuring radio signal power across the entire system, which is costly in outdoor environments due to large network scales. We present RadSplatter, a framework that extends 3D Gaussian Splatting (3DGS) to radio frequencies for efficient and accurate radiomap extrapolation from sparse measurements. RadSplatter models environmental scatterers and radio paths using 3D Gaussians, capturing key factors of radio wave propagation. It employs a relaxed-mean (RM) scheme to reparameterize the positions of 3D Gaussians from noisy and dense 3D point clouds. A camera-free 3DGS-based projection is proposed to map 3D Gaussians onto 2D radio beam patterns. Furthermore, a regularized loss function and recursive fine-tuning using highly structured sparse measurements in real-world settings are applied to ensure robust generalization. Experiments on synthetic and real-world data show state-of-the-art extrapolation accuracy and execution speed.
ComplexBeat: Breathing Rate Estimation from Complex CSI
Authors: Sitian Li, Andreas Toftegaard Kristensen, Andreas Burg, Alexios Balatsoukas-Stimming
Date: 2025-02-18 00:00:00

In this paper, we explore the use of channel state information (CSI) from a WiFi system to estimate the breathing rate of a person in a room. In order to extract WiFi CSI components that are sensitive to breathing, we propose to consider the delay domain channel impulse response (CIR), while most state-of-the-art methods consider its frequency domain representation. One obstacle while processing the CSI data is that its amplitude and phase are highly distorted by measurement uncertainties. We thus also propose an amplitude calibration method and a phase offset calibration method for CSI measured in orthogonal frequency-division multiplexing (OFDM) multiple-input multiple-output (MIMO) systems. Finally, we implement a complete breathing rate estimation system in order to showcase the effectiveness of our proposed calibration and CSI extraction methods.
Hybrid Frequency Transmission for Upload Latency Minimization of IoT Devices in HSR Scenario Aided by Intelligent Reflecting Surfaces
Authors: Tianyou Li, Tonghua Wei, Dapeng Li
Date: 2025-02-18 00:00:00

The explosively growing demand for Internet of Things (IoT) in high-speed railway (HSR) scenario has attracted a lot of attention amongst researchers. However, limited IoT device (IoTD) batteries and large information upload latency still remain critical impediments to practical service applications. In this paper, we consider a HSR wireless mobile communication system, where two intelligent reflecting surfaces (IRSs) are deployed to help solve the problems above. Considering the carrier aggregation method, the IRS needs to be optimized globally in hybrid frequency bands. Meanwhile, to ensure information security, the transmission to the mobile communication relay (MCR) on the train is covert to passengers in the carriage by IRS. This problem is challenging to handle since the variables are coupled with each other and some tricky constraints. We firstly transform the original sum-of-ratios problem into the more tractable parametric problem. Then, the block coordinate descent (BCD) algorithm is adopted to decouple the problem into two main sub-problems, and the downlink and uplink settings are alternatively optimized using low-complexity iterative algorithms. Finally, a heuristic algorithm to mitigate the Doppler spread is proposed to further improve the performance. Simulation results corroborate the performance improvement of the proposed algorithm.
Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks
Authors: Jiacheng Wang, Changyuan Zhao, Hongyang Du, Geng Sun, Jiawen Kang, Shiwen Mao, Dusit Niyato, Dong In Kim
Date: 2025-02-18 00:00:00
Tasks: Data Augmentation, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) uses the same software and hardware resources to achieve both communication and sensing functionalities. Thus, it stands as one of the core technologies of 6G and has garnered significant attention in recent years. In ISAC systems, a variety of machine learning models are trained to analyze and identify signal patterns, thereby ensuring reliable sensing and communications. However, considering factors such as communication rates, costs, and privacy, collecting sufficient training data from various ISAC scenarios for these models is impractical. Hence, this paper introduces a generative AI (GenAI) enabled robust data augmentation scheme. The scheme first employs a conditioned diffusion model trained on a limited amount of collected CSI data to generate new samples, thereby expanding the sample quantity. Building on this, the scheme further utilizes another diffusion model to enhance the sample quality, thereby facilitating the data augmentation in scenarios where the original sensing data is insufficient and unevenly distributed. Moreover, we propose a novel algorithm to estimate the acceleration and jerk of signal propagation path length changes from CSI. We then use the proposed scheme to enhance the estimated parameters and detect the number of targets based on the enhanced data. The evaluation reveals that our scheme improves the detection performance by up to 70%, demonstrating reliability and robustness, which supports the deployment and practical use of the ISAC network.