pwc-archive/papers-with-abstracts huggingface dataset:
Cell-Free Integrated Sensing and Communication: Principles, Advances, and Future Directions
Authors: Diluka Galappaththige, Mohammadali Mohammadi, Gayan Aruma Baduge, Chintha Tellambura
Date: 2025-02-27 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

Cell-free (CF) integrated sensing and communication (ISAC) combines CF architecture with ISAC. CF employs distributed access points, eliminates cell boundaries, and enhances coverage, spectral efficiency, and reliability. ISAC unifies radar sensing and communication, enabling simultaneous data transmission and environmental sensing within shared spectral and hardware resources. CF-ISAC leverages these strengths to improve spectral and energy efficiency while enhancing sensing in wireless networks. As a promising candidate for next-generation wireless systems, CF-ISAC supports robust multi-user communication, distributed multi-static sensing, and seamless resource optimization. However, a comprehensive survey on CF-ISAC has been lacking. This paper fills that gap by first revisiting CF and ISAC principles, covering cooperative transmission, radar cross-section, target parameter estimation, ISAC integration levels, sensing metrics, and applications. It then explores CF-ISAC systems, emphasizing their unique features and the benefits of multi-static sensing. State-of-the-art developments are categorized into performance analysis, resource allocation, security, and user/target-centric designs, offering a thorough literature review and case studies. Finally, the paper identifies key challenges such as synchronization, multi-target detection, interference management, and fronthaul capacity and latency. Emerging trends, including next-generation antenna technologies, network-assisted systems, near-field CF-ISAC, integration with other technologies, and machine learning approaches, are highlighted to outline the future trajectory of CF-ISAC research.
Voting Scheme to Strengthen Localization Security in Randomly Deployed Wireless Sensor Networks
Authors: Slavisa Tomic, Marko Beko, Dejan Vukobratovic, Srdjan Krco
Date: 2025-02-27 00:00:00

This work aspires to provide a trustworthy solution for target localization in adverse environments, where malicious nodes, capable of manipulating distance measurements (i.e., performing spoofing attacks), are present, thus hindering accurate localization. Besides localization, its other goal is to identify (detect) which of the nodes participating in the process are malicious. This problem becomes extremely important with the forthcoming expansion of IoT and smart cities applications, that depend on accurate localization, and the presence of malicious attackers can represent serious security threats if not taken into consideration. This is the case with most existing localization systems which makes them highly vulnerable to spoofing attacks. In addition, existing methods that are intended for adversarial settings consider very specific settings or require additional knowledge about the system model, making them only partially secure. Therefore, this work proposes a novel voting scheme based on clustering and weighted central mass to securely solve the localization problem and detect attackers. The proposed solution has two main phases: 1) Choosing a cluster of suitable points of interest by taking advantage of the problem geometry to assigning votes in order to localize the target, and 2) Attacker detection by exploiting the location estimate and basic statistics. The proposed method is assessed in terms of localization accuracy, success in attacker detection, and computational complexity in different settings. Computer simulations and real-world experiments corroborate the effectiveness of the proposed scheme compared to state-of-the-art methods, showing that it can accomplish an error reduction of $30~\%$ and is capable of achieving almost perfect attacker detection rate when the ratio between attacker intensity and noise standard deviation is significant.
Learning the Topology of a Simplicial Complex Using Simplicial Signals: A Greedy Approach
Authors: A. Buciulea, E. Isufi, G. Leus, A. G. Marques
Date: 2025-02-27 00:00:00

Graphs are ubiquitous to model the irregular (non-Euclidean) structure of complex data, but they are limited to pairwise relationships and fail to model the complexities of the datasets exhibiting higher-order interactions. In that context, simplicial complexes (SCs) are emerging as a tractable candidate to handle such domains. The first step in using SC-based processing and learning schemes is to identify the topology of the SC, which is the problem investigated in this paper. In particular, we assume that we observe a number of signals (features) associated with the nodes of the SC (simplices of order 0) as well as signals (features) associated with a subset of the edges of the SC (simplices of order 1). The goal is then to use these signals to learn the remaining edges as well as the triangles that are filled (simplices of order 2). To address this problem, we assume that the signals are smooth on the unknown SC and that the higher-order relations are sparse. We then postulate the learning problem as a nonconvex optimization and develop an efficient (block-coordinate) algorithm to identify the SC topology.
On Outage Analysis of OTFS Based LEO-Satellite Systems With HAPS Relaying
Authors: Burak Ahmet Celebi, Omer Faruk Akyol, Semiha Tedik Basaran, Ibrahim Hokelek, Ali Gorcin
Date: 2025-02-27 00:00:00

This paper presents an Orthogonal Time Frequency Space (OTFS) waveform application along with a high altitude platform station (HAPS) relaying for remedying severe Doppler effects in non-terrestrial networks (NTNs). Taking practical challenges into consideration, HAPS is exploited as a decode and forward relay node to mitigate the high path loss between a satellite and a base station (BS). In addition, a maximum ratio transmission scheme with multiple antennas at the LEO-satellite is utilized to maximize Signal-to-Noise Ratio (SNR). A shadowed Rician fading model is employed for the channel realization between the LEO-satellite and the HAPS while Nakagami-m is used between the HAPS and the BS. We derive the closed-form expression of the outage probability (OP) for the end-to-end system. The theoretical and simulation results demonstrate that the OP can significantly decrease when the OTFS order and the number of transmit antennas increase.
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
Authors: Mohammad Moulaeifard, Loic Coquelin, Mantas Rinkevičius, Andrius Sološenko, Oskar Pfeffer, Ciaran Bench, Nando Hegemann, Sara Vardanega, Manasi Nandi, Jordi Alastruey, Christian Heiss, Vaidotas Marozas, Andrew Thompson, Philip J. Aston, Peter H. Charlton, Nils Strodthoff
Date: 2025-02-27 00:00:00
Tasks: Benchmarking, Photoplethysmography (PPG)

Photoplethysmography (PPG) is a widely used non-invasive physiological sensing technique, suitable for various clinical applications. Such clinical applications are increasingly supported by machine learning methods, raising the question of the most appropriate input representation and model choice. Comprehensive comparisons, in particular across different input representations, are scarce. We address this gap in the research landscape by a comprehensive benchmarking study covering three kinds of input representations, interpretable features, image representations and raw waveforms, across prototypical regression and classification use cases: blood pressure and atrial fibrillation prediction. In both cases, the best results are achieved by deep neural networks operating on raw time series as input representations. Within this model class, best results are achieved by modern convolutional neural networks (CNNs). but depending on the task setup, shallow CNNs are often also very competitive. We envision that these results will be insightful for researchers to guide their choice on machine learning tasks for PPG data, even beyond the use cases presented in this work.
RIS-Aided Positioning Under Adverse Conditions: Interference from Unauthorized RIS
Authors: Mengting Li, Hui Chen, Alireza Pourafzal, Henk Wymeersch
Date: 2025-02-27 00:00:00
Tasks: Integrated sensing and communication, parameter estimation

Positioning technology, which aims to determine the geometric information of a device in a global coordinate, is a key component in integrated sensing and communication systems. In addition to traditional active anchor-based positioning systems, reconfigurable intelligent surfaces (RIS) have shown great potential for enhancing system performance. However, their ability to manipulate electromagnetic waves and ease of deployment pose potential risks, as unauthorized RIS may be intentionally introduced to jeopardize the positioning service. Such an unauthorized RIS can cause unexpected interference in the original localization system, distorting the transmitted signals, and leading to degraded positioning accuracy. In this work, we investigate the scenario of RIS-aided positioning in the presence of interference from an unauthorized RIS. Theoretical lower bounds are employed to analyze the impact of unauthorized RIS on channel parameter estimation and positioning accuracy. Several codebook design strategies for unauthorized RIS are evaluated, and various system arrangements are discussed. The simulation results show that an unauthorized RIS path with a high channel gain or a delay similar to that of legitimate RIS paths leads to poor positioning performance. Furthermore, unauthorized RIS generates more effective interference when using directional beamforming codebooks compared to random codebooks.
NeRFCom: Feature Transform Coding Meets Neural Radiance Field for Free-View 3D Scene Semantic Transmission
Authors: Weijie Yue, Zhongwei Si, Bolin Wu, Sixian Wang, Xiaoqi Qin, Kai Niu, Jincheng Dai, Ping Zhang
Date: 2025-02-27 00:00:00
Tasks: NeRF

We introduce NeRFCom, a novel communication system designed for end-to-end 3D scene transmission. Compared to traditional systems relying on handcrafted NeRF semantic feature decomposition for compression and well-adaptive channel coding for transmission error correction, our NeRFCom employs a nonlinear transform and learned probabilistic models, enabling flexible variable-rate joint source-channel coding and efficient bandwidth allocation aligned with the NeRF semantic feature's different contribution to the 3D scene synthesis fidelity. Experimental results demonstrate that NeRFCom achieves free-view 3D scene efficient transmission while maintaining robustness under adverse channel conditions.
Research on Event-Related Desynchronization of Motor Imagery and Movement Based on Localized EEG Cortical Sources
Authors: Yuqing Wang
Date: 2025-02-27 00:00:00
Tasks: EEG, Motor Imagery

This study investigates event-related desynchronization (ERD) phenomena during motor imagery and actual movement. Using sLORETA software, we analyzed the cortical current source density distributions in Mu and Beta frequency bands for 33 subjects during rest, motor imagery, and actual movement conditions. The results were normalized for analysis. Using sLORETA's statistical tools, paired t-tests were conducted to compare the normalized current source density results between rest and motor imagery, rest and actual movement, and motor imagery and actual movement conditions in both frequency bands. The findings revealed: In both Mu and Beta frequency bands, during motor imagery, significant ERD (P<0.01) was observed in the salience network, supplementary motor area, primary motor area, premotor cortex, primary somatosensory cortex, and parietofrontal mirror neuron system. During actual movement, significant ERD (P<0.05) was observed in the primary somatosensory cortex, primary motor area, and parietofrontal mirror neuron system in both frequency bands. Comparing motor imagery to actual movement, the current source density in the primary somatosensory cortex and parietofrontal mirror neuron system was higher during motor imagery, though this difference was not statistically significant (P>0.05). This paper analyzes the factors contributing to these statistical results and proposes preliminary solutions.
Transformer-Based Nonlinear Transform Coding for Multi-Rate CSI Compression in MIMO-OFDM Systems
Authors: Bumsu Park, Heedong Do, Namyoon Lee
Date: 2025-02-27 00:00:00
Tasks: Image Compression, Quantization

We propose a novel approach for channel state information (CSI) compression in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, where the frequency-domain channel matrix is treated as a high-dimensional complex-valued image. Our method leverages transformer-based nonlinear transform coding (NTC), an advanced deep-learning-driven image compression technique that generates a highly compact binary representation of the CSI. Unlike conventional autoencoder-based CSI compression, NTC optimizes a nonlinear mapping to produce a latent vector while simultaneously estimating its probability distribution for efficient entropy coding. By exploiting the statistical independence of latent vector entries, we integrate a transformer-based deep neural network with a scalar nested-lattice uniform quantization scheme, enabling low-complexity, multi-rate CSI feedback that dynamically adapts to varying feedback channel conditions. The proposed multi-rate CSI compression scheme achieves state-of-the-art rate-distortion performance, outperforming existing techniques with the same number of neural network parameters. Simulation results further demonstrate that our approach provides a superior rate-distortion trade-off, requiring only 6% of the neural network parameters compared to existing methods, making it highly efficient for practical deployment.
SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning
Authors: Mingsheng Cai, Jiuming Jiang, Wenhao Huang, Che Liu, Rossella Arcucci
Date: 2025-02-27 00:00:00
Tasks: Diagnostic, Representation Learning, Self-Supervised Learning, zero-shot-classification, Zero-Shot Learning

Cardiovascular diseases are a leading cause of death and disability worldwide. Electrocardiogram (ECG) is critical for diagnosing and monitoring cardiac health, but obtaining large-scale annotated ECG datasets is labor-intensive and time-consuming. Recent ECG Self-Supervised Learning (eSSL) methods mitigate this by learning features without extensive labels but fail to capture fine-grained clinical semantics and require extensive task-specific fine-tuning. To address these challenges, we propose $\textbf{SuPreME}$, a $\textbf{Su}$pervised $\textbf{Pre}$-training framework for $\textbf{M}$ultimodal $\textbf{E}$CG representation learning. SuPreME is pre-trained using structured diagnostic labels derived from ECG report entities through a one-time offline extraction with Large Language Models (LLMs), which help denoise, standardize cardiac concepts, and improve clinical representation learning. By fusing ECG signals with textual cardiac queries instead of fixed labels, SuPreME enables zero-shot classification of unseen conditions without further fine-tuning. We evaluate SuPreME on six downstream datasets covering 106 cardiac conditions, achieving superior zero-shot AUC performance of $77.20\%$, surpassing state-of-the-art eSSLs by $4.98\%$. Results demonstrate SuPreME's effectiveness in leveraging structured, clinically relevant knowledge for high-quality ECG representations.
ReVeal: A Physics-Informed Neural Network for High-Fidelity Radio Environment Mapping
Authors: Mukaram Shahid, Kunal Das, Hadia Ushaq, Hongwei Zhang, Jimming Song, Daji Qiao, Sarath Babu, Yong Guan, Zhengyuan Zhu, Arsalan Ahmed
Date: 2025-02-27 00:00:00

Accurately mapping the radio environment (e.g., identifying wireless signal strength at specific frequency bands and geographic locations) is crucial for efficient spectrum sharing, enabling secondary users (SUs) to access underutilized spectrum bands while protecting primary users (PUs). However, current models are either not generalizable due to shadowing, interference, and fading or are computationally too expensive, limiting real-world applicability. To address the shortcomings of existing models, we derive a second-order partial differential equation (PDE) for the Received Signal Strength Indicator (RSSI) based on a statistical model used in the literature. We then propose ReVeal (Re-constructor and Visualizer of Spectrum Landscape), a novel Physics-Informed Neural Network (PINN) that integrates the PDE residual into a neural network loss function to accurately model the radio environment based on sparse RF sensor measurements. ReVeal is validated using real-world measurement data from the rural and suburban areas of the ARA testbed and benchmarked against existing methods.ReVeal outperforms the existing methods in predicting the radio environment; for instance, with a root mean square error (RMSE) of only 1.95 dB, ReVeal achieves an accuracy that is an order of magnitude higher than existing methods such as the 3GPP and ITU-R channel models, ray-tracing, and neural networks. ReVeal achieves both high accuracy and low computational complexity while only requiring sparse RF sampling, for instance, only requiring 30 training sample points across an area of 514 square kilometers.
Pilot and Data Power Control for Uplink Cell-free massive MIMO
Authors: Saeed Mohammadzadeh, Mostafa Rahmani, Kanapathippillai Cumanan, Alister Burr, Pei Xiao
Date: 2025-02-26 00:00:00
Tasks: Fairness

This paper introduces a novel iterative algorithm for optimizing pilot and data power control (PC) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems, aiming to enhance system performance under real-time channel conditions. The approach begins by deriving the signal-to-interference-plus-noise ratio (SINR) using a matched filtering receiver and formulating a min-max optimization problem to minimize the normalized mean square error (NMSE). Utilizing McCormick relaxation, the algorithm adjusts pilot power dynamically, ensuring efficient channel estimation. A subsequent max-min optimization problem allocates data power, balancing fairness and efficiency. The iterative process refines pilot and data power allocations based on updated channel state information (CSI) and NMSE results, optimizing spectral efficiency. By leveraging geometric programming (GP) for data power allocation, the proposed method achieves a robust trade-off between simplicity and performance, significantly improving system capacity and fairness. The simulation results demonstrate that dynamic adjustment of both pilot and data PC substantially enhances overall spectral efficiency and fairness, outperforming the existing schemes in the literature.
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces
Authors: Jiyuan Wang, Weishan Ye, Jialin He, Li Zhang, Gan Huang, Zhuliang Yu, Zhen Liang
Date: 2025-02-26 00:00:00
Tasks: Brain Computer Interface, EEG, Representation Learning

With the rapid advancement of deep learning, attention mechanisms have become indispensable in electroencephalography (EEG) signal analysis, significantly enhancing Brain-Computer Interface (BCI) applications. This paper presents a comprehensive review of traditional and Transformer-based attention mechanisms, their embedding strategies, and their applications in EEG-based BCI, with a particular emphasis on multimodal data fusion. By capturing EEG variations across time, frequency, and spatial channels, attention mechanisms improve feature extraction, representation learning, and model robustness. These methods can be broadly categorized into traditional attention mechanisms, which typically integrate with convolutional and recurrent networks, and Transformer-based multi-head self-attention, which excels in capturing long-range dependencies. Beyond single-modality analysis, attention mechanisms also enhance multimodal EEG applications, facilitating effective fusion between EEG and other physiological or sensory data. Finally, we discuss existing challenges and emerging trends in attention-based EEG modeling, highlighting future directions for advancing BCI technology. This review aims to provide valuable insights for researchers seeking to leverage attention mechanisms for improved EEG interpretation and application.
Beamforming and Waveform Optimization for RF Wireless Power Transfer with Beyond Diagonal Reconfigurable Intelligent Surfaces
Authors: Amirhossein Azarbahram, Onel L. A. Lopez, Bruno Clerckx, Marco Di Renzo, Matti Latva-aho
Date: 2025-02-26 00:00:00

Radio frequency (RF) wireless power transfer (WPT) is a promising technology to seamlessly charge low-power devices, but its low end-to-end power transfer efficiency remains a critical challenge. To address the latter, low-cost transmit/radiating architectures, e.g., based on reconfigurable intelligent surfaces (RISs), have shown great potential. Beyond diagonal (BD) RIS is a novel branch of RIS offering enhanced performance over traditional diagonal RIS (D-RIS) in wireless communications, but its potential gains in RF-WPT remain unexplored. Motivated by this, we analyze a BD-RIS-assisted single-antenna RF-WPT system to charge a single rectifier, and formulate a joint beamforming and multi-carrier waveform optimization problem aiming to maximize the harvested power. We propose two solutions relying on semi-definite programming for fully connected BD-RIS and an efficient low-complexity iterative method relying on successive convex approximation. Numerical results show that the proposed algorithms converge to a local optimum and that adding transmit sub-carriers or RIS elements improves the harvesting performance. We show that the transmit power budget impacts the relative power allocation among different sub-carriers depending on the rectifier's operating regime, while BD-RIS shapes the cascade channel differently for frequency-selective and flat scenarios. Finally, we verify by simulation that BD-RIS and D-RIS achieve the same performance under pure far-field line-of-sight conditions (in the absence of mutual coupling). Meanwhile, BD-RIS outperforms D-RIS as the non-line-of-sight components of the channel become dominant.
Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study
Authors: Mohammad Moulaeifard, Peter H. Charlton, Nils Strodthoff
Date: 2025-02-26 00:00:00
Tasks: Benchmarking, Blood pressure estimation, Domain Adaptation, Out-of-Distribution Generalization, Photoplethysmography (PPG)

Photoplethysmography (PPG)-based blood pressure (BP) estimation represents a promising alternative to cuff-based BP measurements. Recently, an increasing number of deep learning models have been proposed to infer BP from the raw PPG waveform. However, these models have been predominantly evaluated on in-distribution test sets, which immediately raises the question of the generalizability of these models to external datasets. To investigate this question, we trained five deep learning models on the recently released PulseDB dataset, provided in-distribution benchmarking results on this dataset, and then assessed out-of-distribution performance on several external datasets. The best model (XResNet1d101) achieved in-distribution MAEs of 9.4 and 6.0 mmHg for systolic and diastolic BP respectively on PulseDB (with subject-specific calibration), and 14.0 and 8.5 mmHg respectively without calibration. Equivalent MAEs on external test datasets without calibration ranged from 15.0 to 25.1 mmHg (SBP) and 7.0 to 10.4 mmHg (DBP). Our results indicate that the performance is strongly influenced by the differences in BP distributions between datasets. We investigated a simple way of improving performance through sample-based domain adaptation and put forward recommendations for training models with good generalization properties. With this work, we hope to educate more researchers for the importance and challenges of out-of-distribution generalization.