pwc-archive/papers-with-abstracts huggingface dataset:
Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G
Authors: Huixin Xu, Jianhua Zhang, Pan Tang, Hongbo Xing, Haiyang Miao, Nan Zhang, Jian Li, Jianming Wu, Wenfei Yang, Zhening Zhang, Wei Jiang, Zijian He, Afshin Haghighat, Qixing Wang, Guangyi Liu
Date: 2025-06-22 00:00:00

Next generation cellular deployments are expected to exploit the 6-24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies render the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models invalid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS). Despite extensive prior research, incorporating these new features within the standardized channel modeling framework remains an open issue. To address this, this paper presents a channel modeling framework for XL-MIMO systems that incorporates both near-field and SNS features, adopted by 3GPP. For the near-field propagation feature, the framework models the distances from the base station (BS) and user equipment to the spherical-wave sources associated with clusters. These distances are used to characterize element-wise variations of path parameters, such as nonlinear changes in phase and angle. To capture the effect of SNS at the BS side, a stochastic-based approach is proposed to model SNS caused by incomplete scattering, by establishing power attenuation factors from visibility probability and visibility region to characterize antenna element-wise path power variation. In addition, a physical blocker-based approach is introduced to model SNS effects caused by partial blockage. Finally, a simulation framework for near-field and SNS is developed within the structure of the existing 3GPP channel model. Performance evaluations demonstrate that the near-field model captures higher channel capacity potential compared to the far-field model. Coupling loss results indicate that SNS leads to more pronounced propagation fading relative to the spatial stationary model.
Machine Learning-Based Near-Field Localization in Mixed LoS/NLoS Scenarios
Authors: Parisa Ramezani, Seyed Jalaleddin Mousavirad, Mattias O'Nils, Emil Björnson
Date: 2025-06-21 00:00:00

The conventional MUltiple SIgnal Classification (MUSIC) algorithm is effective for angle-of-arrival estimation in the far-field and can be extended for full source localization in the near-field. However, it suffers from high computational complexity, which becomes especially prohibitive in near-field scenarios due to the need for exhaustive 3D grid searches. This paper presents a machine learning-based approach for 3D localization of near-field sources in mixed line-of-sight (LoS)/non-LoS scenarios. A convolutional neural network (CNN) learns the mapping between the eigenvectors of the received signal's covariance matrix at the anchor node and the sources' 3D locations. The detailed description of the proposed CNN model is provided. The effectiveness and time efficiency of the proposed CNN-based localization approach is corroborated via numerical simulations.
Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Authors: Pengyu Han, Zeyi Liu, Shijin Chen, Dongliang Zou, Xiao He
Date: 2025-06-21 00:00:00
Tasks: Diagnostic, Domain Generalization, Fault Diagnosis, Transfer Learning

Multi-condition fault diagnosis is prevalent in industrial systems and presents substantial challenges for conventional diagnostic approaches. The discrepancy in data distributions across different operating conditions degrades model performance when a model trained under one condition is applied to others. With the recent advancements in deep learning, transfer learning has been introduced to the fault diagnosis field as a paradigm for addressing multi-condition fault diagnosis. Among these methods, domain generalization approaches can handle complex scenarios by extracting condition-invariant fault features. Although many studies have considered fault diagnosis in specific multi-condition scenarios, the extent to which operating conditions affect fault information has been scarcely studied, which is crucial. However, the extent to which operating conditions affect fault information has been scarcely studied, which is crucial. When operating conditions have a significant impact on fault features, directly applying domain generalization methods may lead the model to learn condition-specific information, thereby reducing its overall generalization ability. This paper investigates the performance of existing end-to-end domain generalization methods under varying conditions, specifically in variable-speed and variable-load scenarios, using multiple experiments on a real-world gearbox. Additionally, a two-stage diagnostic framework is proposed, aiming to improve fault diagnosis performance under scenarios with significant operating condition impacts. By incorporating a domain-generalized encoder with a retraining strategy, the framework is able to extract condition-invariant fault features while simultaneously alleviating potential overfitting to the source domain. Several experiments on a real-world gearbox dataset are conducted to validate the effectiveness of the proposed approach.
Two-Stage Prony-Based Estimation of Fractional Delay and Doppler Shifts in OTFS Modulation
Authors: Yutaka Jitsumatsu, Liangchen Sun
Date: 2025-06-21 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper addresses the estimation of fractional delay and Doppler shifts in multipath channels that cause doubly selective fading-an essential task for integrated sensing and communication (ISAC) systems in high-mobility environments. Orthogonal Time Frequency Space (OTFS) modulation enables simple and robust channel compensation under such conditions. However, fractional delay and Doppler components introduce inter-path interference, degrading estimation accuracy. We propose a two-stage estimation method based on Prony's technique using OTFS pilot signals with M subchannels and N pilot repetitions. In the first stage, Doppler frequencies are estimated by jointly solving M coupled Prony equations, exploiting the periodicity of the pilot signal. In the second stage, delays are estimated by applying the discrete Fourier transform (DFT) and Prony's method to each Doppler component obtained in the first stage. The proposed method can accurately estimate up to N-1 delay-Doppler parameters under noiseless conditions. In noisy environments, conventional information criteria such as AIC and BIC yield suboptimal performance; thus, a heuristic model order selection is adopted. Numerical simulations confirm that the proposed method achieves high estimation accuracy, highlighting its potential for future ISAC frameworks.
Efficient Implementation of Multi-sensor Adaptive Birth Samplers for Labeled Random Finite Set Tracking
Authors: Jennifer Bondarchuk, Anthony Trezza, Donald J. Bucci Jr
Date: 2025-06-20 00:00:00

Adaptive track initiation remains a crucial component of many modern multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A naive construction of this adaptive birth set density results in an exponential number of newborn components in the number of sensors. A truncation procedure was provided that leverages a Gibbs sampler to truncate the birth density, reducing the complexity to quadratic in the number of sensors. However, only a limited discussion has been provided on additional algorithmic techniques that can be employed to substantially reduce the complexity in practical tracking applications. In this paper, we propose five efficiency enhancements for the labeled random finite sets multi-sensor adaptive birth procedure. Simulation results are provided to demonstrate their computational benefits and show that they result in a negligible change to the multi-target tracking performance.
Intelligent Reflecting Surfaces for THz Communications: Fundamentals, Key Solutions, and System Prototyping
Authors: Qingqing Wu, Yanze Zhu, Qiaoyan Peng, Wanming Hao, Yanzhao Hou, Fengyuan Yang, Wencai Yan, Guoning Wang, Wen Chen, Chi Qiu
Date: 2025-06-20 00:00:00

Intelligent reflecting surfaces (IRSs) have emerged as a cost-effective technology for terahertz (THz) communications by enabling programmable control of the wireless environment. This paper provides a comprehensive overview of IRSs-aided THz communications, covering hardware designs, advanced signal processing techniques, and practical deployment strategies. It first examines key THz reconfigurable metasurface architectures, including electronic, optical, phase-change material, and micro-electromechanical systems (MEMS)-based implementations, highlighting their reconfiguration mechanisms and challenges. Then, fundamental effects including near field and beam squint in wideband THz systems are analyzed, along with their impacts on system performance. The paper further explores conventional and beam-squint-assisted channel estimation methods, innovative beam management strategies, and deployment considerations across large- and small-scale scenarios. Practical experiments at 220 gigahertz (GHz) validate the effectiveness of IRS in improving signal strength and communication reliability for both single-user and multi-user setups.
On Energy-Efficient Passive Beamforming Design of RIS-Assisted CoMP-NOMA Networks
Authors: Muhammad Umer, Muhammad Ahmed Mohsin, Aamir Mahmood, Haejoon Jung, Haris Pervaiz, Mikael Gidlund, Syed Ali Hassan
Date: 2025-06-20 00:00:00

This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and performance of next-generation wireless networks. We delve into the design of energy-efficient passive beamforming (PBF) strategies within RIS-assisted coordinated multi-point (CoMP)-NOMA networks. Two distinct RIS configurations, namely, enhancement-only PBF (EO) and enhancement & cancellation PBF (EC), are proposed and analyzed. Our findings demonstrate that RIS-assisted CoMP-NOMA networks offer significant efficiency gains compared to traditional CoMP-NOMA systems. Furthermore, we formulate a PBF design problem to optimize the RIS phase shifts for maximizing energy efficiency. Our results reveal that the optimal PBF design is contingent upon several factors, including the number of cooperating base stations (BSs), the number of RIS elements deployed, and the RIS configuration. This study underscores the potential of RIS-assisted CoMP-NOMA networks as a promising solution for achieving superior energy efficiency and overall performance in future wireless networks.
MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition
Authors: Navid Hasanzadeh, Shahrokh Valaee
Date: 2025-06-15 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Time Series Classification

The newly established IEEE 802.11bf Task Group aims to amend the WLAN standard to support advanced sensing applications such as human activity recognition (HAR). Although studies have demonstrated the potential of sub-7 GHz Wi-Fi Channel State Information (CSI) for HAR, no method currently performs reliably in real-world scenarios. This work tackles the poor generalization of Wi-Fi-based HAR by introducing an innovative approach to extracting and utilizing movement-related representations, which makes it robust to noise and static environmental properties. This is achieved by transforming CSI signals into the delay profile space and decomposing them into various Doppler velocities, which serve as informative projections of a mobile point's velocity from different unknown random angles. To mitigate the impact of this randomness, MORIC is introduced as a novel time series classification model based on random convolutional kernels, designed to be invariant to the random order and repetition of input representations, thereby enabling robust Wi-Fi CSI-based activity classification. Experimental results on the collected dataset demonstrate that the proposed method outperforms state-of-the-art approaches in terms of generalization accuracy for hand motion recognition, particularly for challenging gestures. Furthermore, incorporating a small number of calibration samples leads to a significant improvement in accuracy, enhancing the practicality of the method for real-world deployment.
Low-Complexity Receiver Design for Affine Filter Bank Modulation
Authors: Kuranage Roche Rayan Ranasinghe, Bruno S. Chang, Giuseppe Thadeu Freitas de Abreu
Date: 2025-06-20 00:00:00
Tasks: ISAC

We propose a low-complexity receiver structure for the recently introduced Affine Filter Bank Modulation (AFBM) scheme, which is a novel waveform designed for integrated sensing and communications (ISAC) systems operating in doubly-dispersive (DD) channels. The proposed receiver structure is based on the Gaussian Belief Propagation (GaBP) framework, making use of only element-wise scalar operations to perform detection of the transmitted symbols. Simulation results demonstrate that AFBM in conjunction with GaBP outperforms affine frequency division multiplexing (AFDM) in terms of bit error rates (BERs) in DD channels, while achieving very low out-of-band emissions (OOBE) in high-mobility scenarios.
Wi-Fi Sensing Tool Release: Gathering 802.11ax Channel State Information from a Commercial Wi-Fi Access Point
Authors: Zisheng Wang, Feng Li, Hangbin Zhao, Zihuan Mao, Yaodong Zhang, Qisheng Huang, Bo Cao, Mingming Cao, Baolin He, Qilin Hou
Date: 2025-06-20 00:00:00
Tasks: Gesture Recognition

Wi-Fi sensing has emerged as a powerful technology, leveraging channel state information (CSI) extracted from wireless data packets to enable diverse applications, ranging from human presence detection to gesture recognition and health monitoring. However, CSI extraction from commercial Wi-Fi access point lacks and out of date. This paper introduces ZTECSITool,a toolkit designed to capture high-resolution CSI measurements from commercial Wi-Fi 6 (802.11ax) access points, supporting bandwidths up to 160 MHz and 512 subcarriers. ZTECSITool bridges a critical gap in Wi-Fi sensing research, facilitating the development of next-generation sensing systems. The toolkit includes customized firmware and open-source software tools for configuring, collecting, and parsing CSI data, offering researchers a robust platform for advanced sensing applications. We detail the command protocols for CSI extraction, including band selection,STA filtering, and report configuration, and provide insights into the data structure of the reported CSI. Additionally, we present a Python-based graphical interface for real-time CSI visualization and analysis
Beamforming design for minimizing the signal power estimation error
Authors: Esa Ollila, Xavier Mestre, Elias Raninen
Date: 2025-06-20 00:00:00

We study the properties of beamformers in their ability to either maintain or estimate the true signal power of the signal of interest (SOI). Our focus is particularly on the Capon beamformer and the minimum mean squared error (MMSE) beamformer. The Capon beamformer, also known as the minimum power distortionless response (MPDR) or the minimum variance distortionless response (MVDR) beamformer, is a widely used method in array signal processing. A curious feature of both the Capon and the MMSE beamformers is their tendency to either overestimate or underestimate the signal power. That is, they are not asymptotically unbiased (as the sample size approaches infinity). To address this issue, we propose to shrink the Capon beamformer by finding a scaling factor that minimizes the mean squared error (MSE) of the signal power estimate. The new beamformer, referred to as the Capon$^+$ beamformer, is evaluated against the Capon and MMSE beamformers in terms of bias, signal power MSE, and signal waveform MSE. The Capon$^+$ beamformer strikes a better balance between signal power and waveform estimation while also exhibiting minimal bias, which approaches zero as the sample size increases.
Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias
Authors: Amir Reza Vazifeh, Jason W. Fleischer
Date: 2025-06-19 00:00:00
Tasks: Dimensionality Reduction

Electrocardiograms (ECGs) provide direct, non-invasive measurements of heart activity and are well-established tools for detecting and monitoring cardiovascular disease. However, manual ECG analysis can be time-consuming and prone to errors. Machine learning has emerged as a promising approach for automated heartbeat recognition and classification, but substantial variations in ECG signals make it challenging to develop generalizable models. ECG signals can vary widely across individuals and leads, while datasets often follow different labeling standards and may be biased, all of which greatly hinder supervised methods. Conventional unsupervised methods, e.g. principal component analysis, prioritize large (and often obvious) variances in the data and typically overlook subtle yet clinically relevant patterns. If labels are missing and/or variations are significant but small, both approaches fail. Here, we show that nonlinear dimensionality reduction (NLDR) can accommodate these issues and identify medically relevant features in ECG signals, with no need for training or prior information. Using the MLII and V1 leads of the MIT-BIH dataset, we demonstrate that t-distributed stochastic neighbor embedding and uniform manifold approximation and projection can discriminate individual recordings in mixed populations with >= 90% accuracy and distinguish different arrhythmias in individual patients with a median accuracy of 98.96% and a median F1-score of 91.02%. The results show that NLDR holds much promise for cardiac monitoring, including the limiting cases of single-lead ECG and the current 12-lead standard of care, and for personalized health care beyond cardiology.
Widely Linear Augmented Extreme Learning Machine Based Impairments Compensation for Satellite Communications
Authors: Yang Luo, Arunprakash Jayaprakash, Gaojie Chen, Chong Huang, Qu Luo, Pei Xiao
Date: 2025-06-17 00:00:00

Satellite communications are crucial for the evolution beyond fifth-generation networks. However, the dynamic nature of satellite channels and their inherent impairments present significant challenges. In this paper, a novel post-compensation scheme that combines the complex-valued extreme learning machine with augmented hidden layer (CELMAH) architecture and widely linear processing (WLP) is developed to address these issues by exploiting signal impropriety in satellite communications. Although CELMAH shares structural similarities with WLP, it employs a different core algorithm and does not fully exploit the signal impropriety. By incorporating WLP principles, we derive a tailored formulation suited to the network structure and propose the CELM augmented by widely linear least squares (CELM-WLLS) for post-distortion. The proposed approach offers enhanced communication robustness and is highly effective for satellite communication scenarios characterized by dynamic channel conditions and non-linear impairments. CELM-WLLS is designed to improve signal recovery performance and outperform traditional methods such as least square (LS) and minimum mean square error (MMSE). Compared to CELMAH, CELM-WLLS demonstrates approximately 0.8 dB gain in BER performance, and also achieves a two-thirds reduction in computational complexity, making it a more efficient solution.
A Tractable Approach to Massive Communication and Ubiquitous Connectivity in 6G Standardization
Authors: Junyi Jiang, Wei Chen, Xin Guo, Shenghui Song, Ying Jun, Zhang, Zhu Han, Merouane Debbah, Khaled B. Letaief
Date: 2025-06-19 00:00:00

The full-scale 6G standardization has attracted considerable recent attention, especially since the first 3GPP-wide 6G workshop held in March 2025. To understand the practical and fundamental values of 6G and facilitate its standardization, it is crucial to explore the theoretical limits of spectrum, energy, and coverage efficiency considering practical hardware and signaling constraints. In this paper, we present a mean-field-approximation-based investigation on two out of six use case scenarios defined by IMT-2030, namely, massive communication and ubiquitous connectivity. Being aware of the limitation in interference cancellation owing to constrained cost and hardware complexity, we investigate the spectrum reuse architecture in both usage scenarios. We propose a tractable spectrum reuse with low signaling overhead consumed for channel estimation and channel state information (CSI) feedback. Our analysis indicates that the massive communication over cellular and device-to-device (D2D) networks can benefit from channel orthogonalization, while it is unnecessary to share the CSI of interfering links. Moreover, deploying relays or movable base stations, e.g. unmanned aerial vehicle, yields substantial energy and spectrum gain for ubiquitous connectivity, despite introducing interference. As such, the mean-field-optimization-based evaluation is expected to positively impact 6G and NextG standardization in 3GPP and other standardization bodies.
Refining Ray-Tracing Accuracy and Efficiency in the Context of FRMCS Urban Railway Channel Predictions
Authors: Romain Charbonnier, Thierry Tenoux, Yoann Corre
Date: 2025-06-19 00:00:00

The upcoming roll-out of the new wireless communication standard for wireless railway services, FRMCS, requires a thorough understanding of the system performance in real-world conditions, since this will strongly influence the deployment costs and the effectiveness of an infrastructure planned for decades. The virtual testing of the equipment and network performance in realistic simulated scenarios is key; its accuracy depends on the reliability of the predicted radio channel properties. In this article, the authors explain how they are evolving a ray-tracing (RT) tool to apply it to the specific case of simulating the radio link between the FRMCS fixed infrastructure and an antenna placed on the roof of a train moving in an urban environment. First, a dynamic version of the RT tool is used to capture the rapid variations of all channel metrics; a compromise is sought between computation time and accuracy. Besides, a hybridization of RT and physical optics (PO) allows the integration of objects near the track, such as catenary pylons, into the simulation. A case study shows that the scattering by metallic pylons brings a significant contribution.