pwc-archive/papers-with-abstracts huggingface dataset:
Application of Quaternions to Obtain Analytic Solutions to Systems of Polarization Components
Authors: Michael G. Taylor
Date: 2025-05-21 00:00:00

The usual way to describe mathematically a beam of coherent light passing through a system of waveplates is via the Jones vector and Jones matrix. This paper will show that a quaternion can be used to represent both the optical signal and the waveplate component it passes through, replacing the Jones vector and the Jones matrix. The quaternion description is easier to manipulate than the matrix-vector description; for example it can be inverted. As well as the Jones vector, the state of polarization (SOP) of an optical signal is often described as a three-dimensional vector on the Poincar\'e sphere, or as a polarization ellipse, and it will be shown how these three forms are closely related to the quaternion representation. Similarly, the action of a waveplate may be represented as a rotation about an axis on the Poincar\'e sphere, and that rotation is shown to have a logarithm-exponential relationship to the waveplate's quaternion. The paper presents rules to decide if two optical signals are aligned or orthogonal in phase or in polarization from their quaternions, and presents the quaternion operations to change the phase or change the SOP. Light passing through a system of waveplates is written as a product of quaternions, and it can be hard to simplify or manipulate that expression because quaternion multiplication does not commute. The paper brings together several mathematical tools that allow such a quaternion product to be rearranged, including the new idea of partial conjugation. Finally, a worked example is included of the quaternion mathematics applied to a waveplate problem that has not been solved before. It is shown that an endless optical phase shifter can be built using three rotatable waveplates, and equations for the angles of rotation are derived to produce the desired phase shift for given input and output SOPs.
AI-Assisted NLOS Sensing for RIS-Based Indoor Localization in Smart Factories
Authors: Taofeek A. O. Yusuf, Sigurd S. Petersen, Puchu Li, Jian Ren, Placido Mursia, Vincenzo Sciancalepore, Xavier Costa Pérez, Gilberto Berardinelli, Ming Shen
Date: 2025-05-21 00:00:00
Tasks: Indoor Localization

In the era of Industry 4.0, precise indoor localization is vital for automation and efficiency in smart factories. Reconfigurable Intelligent Surfaces (RIS) are emerging as key enablers in 6G networks for joint sensing and communication. However, RIS faces significant challenges in Non-Line-of-Sight (NLOS) and multipath propagation, particularly in localization scenarios, where detecting NLOS conditions is crucial for ensuring not only reliable results and increased connectivity but also the safety of smart factory personnel. This study introduces an AI-assisted framework employing a Convolutional Neural Network (CNN) customized for accurate Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) classification to enhance RIS-based localization using measured, synthetic, mixed-measured, and mixed-synthetic experimental data, that is, original, augmented, slightly noisy, and highly noisy data, respectively. Validated through such data from three different environments, the proposed customized-CNN (cCNN) model achieves {95.0\%-99.0\%} accuracy, outperforming standard pre-trained models like Visual Geometry Group 16 (VGG-16) with an accuracy of {85.5\%-88.0\%}. By addressing RIS limitations in NLOS scenarios, this framework offers scalable and high-precision localization solutions for 6G-enabled smart factories.
Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning
Authors: Changyuan Zhao, Ruichen Zhang, Jiacheng Wang, Dusit Niyato, Geng Sun, Hongyang Du, Zan Li, Abbas Jamalipour, Dong In Kim
Date: 2025-05-21 00:00:00
Tasks: Spectrum Cartography

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments validate that this integrated GenAI framework outperforms traditional interpolation methods and deep learning baselines by achieving 57.35% and 88.68% reconstruction error reduction, respectively. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments.
Robust Activity Detection for Massive Random Access
Authors: Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov
Date: 2025-05-21 00:00:00
Tasks: Action Detection, Activity Detection

Massive machine-type communications (mMTC) are fundamental to the Internet of Things (IoT) framework in future wireless networks, involving the connection of a vast number of devices with sporadic transmission patterns. Traditional device activity detection (AD) methods are typically developed for Gaussian noise, but their performance may deteriorate when these conditions are not met, particularly in the presence of heavy-tailed impulsive noise. In this paper, we propose robust statistical techniques for AD that do not rely on the Gaussian assumption and replace the Gaussian loss function with robust loss functions that can effectively mitigate the impact of heavy-tailed noise and outliers. First, we prove that the coordinate-wise (conditional) objective function is geodesically convex and derive a fixed-point (FP) algorithm for minimizing it, along with convergence guarantees. Building on the FP algorithm, we propose two robust algorithms for solving the full (unconditional) objective function: a coordinate-wise optimization algorithm (RCWO) and a greedy covariance learning-based matching pursuit algorithm (RCL-MP). Numerical experiments demonstrate that the proposed methods significantly outperform existing algorithms in scenarios with non-Gaussian noise, achieving higher detection accuracy and robustness.
Exploiting Age of Information in Network Digital Twins for AI-driven Real-Time Link Blockage Detection
Authors: Michele Zhu, Francesco Linsalata, Silvia Mura, Lorenzo Cazzella, Damiano Badini, Umberto Spagnolini
Date: 2025-05-21 00:00:00

The Line-of-Sight (LoS) identification is crucial to ensure reliable high-frequency communication links, especially those vulnerable to blockages. Network Digital Twins and Artificial Intelligence are key technologies enabling blockage detection (LoS identification) for high-frequency wireless systems, e.g., 6>GHz. In this work, we enhance Network Digital Twins by incorporating Age of Information (AoI) metrics, a quantification of status update freshness, enabling reliable real-time blockage detection (LoS identification) in dynamic wireless environments. By integrating raytracing techniques, we automate large-scale collection and labeling of channel data, specifically tailored to the evolving conditions of the environment. The introduced AoI is integrated with the loss function to prioritize more recent information to fine-tune deep learning models in case of performance degradation (model drift). The effectiveness of the proposed solution is demonstrated in realistic urban simulations, highlighting the trade-off between input resolution, computational cost, and model performance. A resolution reduction of 4x8 from an original channel sample size of (32, 1024) along the angle and subcarrier dimension results in a computational speedup of 32 times. The proposed fine-tuning successfully mitigates performance degradation while requiring only 1% of the available data samples, enabling automated and fast mitigation of model drifts.
SINR Maximizing Distributionally Robust Adaptive Beamforming
Authors: Kiarash Hassas Irani, Yongwei Huang, Sergiy A. Vorobyov
Date: 2025-05-21 00:00:00

This paper addresses the robust adaptive beamforming (RAB) problem via the worst-case signal-to-interference-plus-noise ratio (SINR) maximization over distributional uncertainty sets for the random interference-plus-noise covariance (INC) matrix and desired signal steering vector. Our study explores two distinct uncertainty sets for the INC matrix and three for the steering vector. The uncertainty sets of the INC matrix account for the support and the positive semidefinite (PSD) mean of the distribution, as well as a similarity constraint on the mean. The uncertainty sets for the steering vector consist of the constraints on the first- and second-order moments of its associated probability distribution. The RAB problem is formulated as the minimization of the worst-case expected value of the SINR denominator over any distribution within the uncertainty set of the INC matrix, subject to the condition that the expected value of the numerator is greater than or equal to one for every distribution within the uncertainty set of the steering vector. By leveraging the strong duality of linear conic programming, this RAB problem is reformulated as a quadratic matrix inequality problem. Subsequently, it is addressed by iteratively solving a sequence of linear matrix inequality relaxation problems, incorporating a penalty term for the rank-one PSD matrix constraint. We further analyze the convergence of the iterative algorithm. The proposed robust beamforming approach is validated through simulation examples, which illustrate improved performance in terms of the array output SINR.
AI-empowered Real-Time Line-of-Sight Identification via Network Digital Twins
Authors: Michele Zhu, Silvia Mura, Francesco Linsalata, Lorenzo Cazzella, Damiano Badini, Umberto Spagnolini
Date: 2025-05-21 00:00:00
Tasks: Deep Learning

The identification of Line-of-Sight (LoS) conditions is critical for ensuring reliable high-frequency communication links, which are particularly vulnerable to blockages and rapid channel variations. Network Digital Twins (NDTs) and Ray-Tracing (RT) techniques can significantly automate the large-scale collection and labeling of channel data, tailored to specific wireless environments. This paper examines the quality of Artificial Intelligence (AI) models trained on data generated by Network Digital Twins. We propose and evaluate training strategies for a general-purpose Deep Learning model, demonstrating superior performance compared to the current state-of-the-art. In terms of classification accuracy, our approach outperforms the state-of-the-art Deep Learning model by 5% in very low SNR conditions and by approximately 10% in medium-to-high SNR scenarios. Additionally, the proposed strategies effectively reduce the input size to the Deep Learning model while preserving its performance. The computational cost, measured in floating-point operations per second (FLOPs) during inference, is reduced by 98.55% relative to state-of-the-art solutions, making it ideal for real-time applications.
On Optimizing Time-, Space- and Power-Domain Energy-Saving Techniques for Sub-6 GHz Base Stations
Authors: Emanuele Peschiera, Youssef Agram, François Quitin, Liesbet Van der Perre, François Rottenberg
Date: 2025-05-21 00:00:00

What is the optimal base station (BS) resource allocation strategy given a measurement-based power consumption model and a fixed target user rate? Rush-to-sleep in time, rush-to-mute in space, awake-but-whisper in power, or a combination of them? We propose in this paper an efficient solution to the problem of finding the optimal number of active time slots, active antennas, and transmit power at active antennas in a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system under per-user rate and per-antenna transmit power constraints. The use of a parametric power consumption model validated on operator measurements of 4G and 5G BSs enhances the interpretation of the results. We discuss the optimal energy-saving strategy at different network loads for three BS configurations. Using as few BS antennas as possible is close to optimal in BSs not implementing time-domain power savings such as micro-discontinuous transmission ({\mu}DTX). Energy-saving schemes that jointly operate in the three domains are instead optimal when the BS hardware can enter time-domain power-saving modes, with a tendency for rush-to-mute in massive MIMO and for rush-to-sleep in BS with fewer antennas. Median energy savings up to $30\%$ are achieved at low network loads.
Wireless Sensing via Pinching-Antenna Systems
Authors: Zhaolin Wang, Chongjun Ouyang, Yuanwei Liu, Arumugam Nallanathan
Date: 2025-05-21 00:00:00

A wireless sensing architecture via pinching antenna systems is proposed. Compared to conventional wireless systems, PASS offers flexible antenna deployment and improved probing performance for wireless sensing by leveraging dielectric waveguides and pinching antennas (PAs). To enhance signal reception, leaky coaxial (LCX) cables are used to uniformly collect echo signals over a wide area. The Cram\'er-Rao bound (CRB) for multi-target sensing is derived and then minimized through the joint optimization of the transmit waveform and the positions of PAs. To solve the resulting highly coupled, non-convex problem, a two-stage particle swarm optimization (PSO)-based algorithm is proposed. Numerical results demonstrate significant gains in sensing accuracy and robustness over conventional sensing systems, highlighting the benefits of integrating LCX-based reception with optimized PASS configurations.
RIS Beam Calibration for ISAC Systems: Modeling and Performance Analysis
Authors: Mengting Li, Hui Chen, Sigurd Sandor Petersen, Huiping Huang, Alireza Pourafzal, Yu Ge, Ming Shen, Henk Wymeersch
Date: 2025-05-21 00:00:00
Tasks: Autonomous Driving, Integrated sensing and communication, ISAC

High-accuracy localization is a key enabler for integrated sensing and communication (ISAC), playing an essential role in various applications such as autonomous driving. Antenna arrays and reconfigurable intelligent surface (RIS) are incorporated into these systems to achieve high angular resolution, assisting in the localization process. However, array and RIS beam patterns in practice often deviate from the idealized models used for algorithm design, leading to significant degradation in positioning accuracy. This mismatch highlights the need for beam calibration to bridge the gap between theoretical models and real-world hardware behavior. In this paper, we present and analyze three beam models considering several key non-idealities such as mutual coupling, non-ideal codebook, and measurement uncertainties. Based on the models, we then develop calibration algorithms to estimate the model parameters that can be used for future localization tasks. This work evaluates the effectiveness of the beam models and the calibration algorithms using both theoretical bounds and real-world beam pattern data from an RIS prototype. The simulation results show that the model incorporating combined impacts can accurately reconstruct measured beam patterns. This highlights the necessity of realistic beam modeling and calibration to achieve high-accuracy localization.
Robust Secure Communications in Near-Field ISCAP Systems with Extremely Large-Scale Antenna Array
Authors: Zixiang Ren, Siyao Zhang, Ling Qiu, Derrick Wing Kwan Ng, Jie Xu
Date: 2025-05-21 00:00:00

This paper investigates robust secure communications in a near-field integrated sensing, communication, and powering (ISCAP) system, in which the base station (BS) is equipped with an extremely large-scale antenna array (ELAA). In this system, the BS transmits confidential messages to a single legitimate communication user (CU), simultaneously providing wireless power transfer to multiple energy receivers (ERs) and performing point target sensing. We consider a scenario in which both the ERs and the sensing target may act as potential eavesdroppers attempting to intercept the confidential messages. To safeguard secure communication, the BS employs a joint beamforming design by transmitting information beams combined with dedicated triple-purpose beams serving as energy and sensing signals, as well as artificial noise (AN) for effectively jamming potential eavesdroppers. It is assumed that only coarse location information of the ERs and sensing targets or eavesdroppers is available at the BS, leading to imperfect channel state information (CSI). Under this setup, we formulate a robust beamforming optimization problem with the objective of maximizing the secrecy rate for the CU, while ensuring worst-case performance requirements on both target sensing and wireless energy harvesting at the ERs. To address the non-convex robust joint beamforming problem and facilitate the deployment of a low-complexity algorithm, we employ the S-procedure alongside an eavesdropping CSI error-bound determination method to acquire a high-quality solution.
Study of Brain Connectivity by Multichannel EEG Quaternion Principal Component Analysis for Alzheimer Disease Classification
Authors: Kevin Hung, Gary Man-Tat Man, Jincheng Wang
Date: 2025-05-21 00:00:00
Tasks: Classification, EEG

The early detection of Alzheimer's disease (AD) through widespread screening has emerged as a primary strategy to mitigate the significant global impact of AD. EEG measurements offer a promising solution for extensive AD detection. However, the intricate and nonlinear dynamics of multichannel EEG signals pose a considerable challenge for real-time AD diagnosis. This paper introduces a novel algorithm, which is based on Quaternion Principal Component Analysis (QPCA) of multichannel EEG signals, for AD classification. The algorithm extracts high dimensional correlations among different channels to generate features that are maximally representative with minimal information redundancy. This provides a multidimensional and precise measure of brain connectivity in disease assessment. Simulations have been conducted to evaluate the performance and to identify the most critical EEG channels or brain regions for AD classification. The results reveal a significant drop of connectivity measure in the alpha bands. The average AD classification accuracy for all 4-channel combinations reached 95%, while some particular permutations of channels achieved 100% accuracy rate. Furthermore, the temporal lobe emerges as one of the most important regions in AD classification given that the EEG signals are recorded during the presentation of an auditory stimulant. The selection of key parameters of the QPCA algorithm have been evaluated and some recommendations are proposed for further performance enhancement. This paper marks the first application of the QPCA algorithm for AD classification and brain connectivity analysis using multichannel EEG signals.
Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression
Authors: Samuel Fernández-Menduiña, Xin Xiong, Eduardo Pavez, Antonio Ortega, Neil Birkbeck, Balu Adsumilli
Date: 2025-05-21 00:00:00
Tasks: Quantization

Service providers must encode a large volume of noisy videos to meet the demand for user-generated content (UGC) in online video-sharing platforms. However, low-quality UGC challenges conventional codecs based on rate-distortion optimization (RDO) with full-reference metrics (FRMs). While effective for pristine videos, FRMs drive codecs to preserve artifacts when the input is degraded, resulting in suboptimal compression. A more suitable approach used to assess UGC quality is based on non-reference metrics (NRMs). However, RDO with NRMs as a measure of distortion requires an iterative workflow of encoding, decoding, and metric evaluation, which is computationally impractical. This paper overcomes this limitation by linearizing the NRM around the uncompressed video. The resulting cost function enables block-wise bit allocation in the transform domain by estimating the alignment of the quantization error with the gradient of the NRM. To avoid large deviations from the input, we add sum of squared errors (SSE) regularization. We derive expressions for both the SSE regularization parameter and the Lagrangian, akin to the relationship used for SSE-RDO. Experiments with images and videos show bitrate savings of more than 30\% over SSE-RDO using the target NRM, with no decoder complexity overhead and minimal encoder complexity increase.
A Compact Narrowband Antenna Design for RF Fingerprinting Applications
Authors: Sheng Huang, Cory Hilton, Steve Bush, Faiz Sherman, Jeffrey A. Nanzer
Date: 2025-05-20 00:00:00
Tasks: TAG

Radio frequency (RF) fingerprinting is widely used for supporting physical layer security in various wireless applications. In this paper, we present the design and implementation of a small antenna with low-cost fabrication that can be directly integrated with nonlinear passive devices, forming a passive RF tag providing unique nonlinear signatures for RF fingerprinting. We first propose a miniaturized meander line dipole, achieved by two folded arms on two sides of the substrate. This leads to antenna with a simple feeding structure and compact size, making it ideal for planar integration. Two antennas on Rogers 4350B and ultra-thin flexible Panasonic Felios are fabricated, achieving small size at $0.21 \times 0.06 \times 0.004 \lambda_0^3$ and $0.14 \times 0.1 \times 0.0008 \lambda_0^3$ with realized gain of 1.87 dBi and 1.46 dBi. The passive tag consists of the proposed antenna structure and an integrated RF diode, and is further developed on both substrates, aiming to generate inter-modulation products (IMP) due to the nonlinearity of the diode, which can be used for device identification through classification algorithms. We investigate the nonlinearity of the designed tags for transmission at 15 dBm using two-tone signals. All tags produce a significant increased power at IMP frequencies at a range of 0.4 m. The tags on Rogers substrate provide around 23 dB IMP power increase and tags on flexible substrate embedded in lossy material provide around 16 dB power increase. These findings confirm that the proposed solution offers a simple passive tag design to support unique nonlinear signatures for RF fingerprinting applications in a simple, low-cost device.