pwc-archive/papers-with-abstracts huggingface dataset:
High-Dimensional Sequential Change Detection
Authors: Robert Malinas, Dogyoon Song, Benjamin D. Robinson, Alfred O. Hero III
Date: 2025-02-07 00:00:00
Tasks: Change Detection

We address the problem of detecting a change in the distribution of a high-dimensional multivariate normal time series. Assuming that the post-change parameters are unknown and estimated using a window of historical data, we extend the framework of quickest change detection (QCD) to the highdimensional setting in which the number of variables increases proportionally with the size of the window used to estimate the post-change parameters. Our analysis reveals that an information theoretic quantity, which we call the Normalized High- Dimensional Kullback-Leibler divergence (NHDKL), governs the high-dimensional asymptotic performance of QCD procedures. Specifically, we show that the detection delay is asymptotically inversely proportional to the difference between the NHDKL of the true post-change versus pre-change distributions and the NHDKL of the true versus estimated post-change distributions. In cases of perfect estimation, where the latter NHDKL is zero, the delay is inversely proportional to the NHDKL between the post-change and pre-change distributions alone. Thus, our analysis is a direct generalization of the traditional fixed-dimension, large-sample asymptotic framework, where the standard KL divergence is asymptotically inversely proportional to detection delay. Finally, we identify parameter estimators that asymptotically minimize the NHDKL between the true versus estimated post-change distributions, resulting in a QCD method that is guaranteed to outperform standard approaches based on fixed-dimension asymptotics.
Non-Interchangeability between Heart Rate Variability and Pulse Rate Variability During Supine-to-Stand Tests
Authors: Runwei Lin, Frank Halfwerk, Gozewijn Dirk Laverman, Dirk Donker, Ying Wang
Date: 2025-02-11 00:00:00
Tasks: Heart Rate Variability, STS

Heart rate variability (HRV) is widely recognized as a valuable biomarker for assessing autonomic cardiac regulation. Pulse rate variability (PRV) is a common surrogate of HRV given the wide usability of PPG in commercially available devices. However, there is no clear conclusion on whether PRV can replace HRV given their different physiological mechanisms. This study evaluates the interchangeability of young adults HRV and PRV during supine-to-stand (STS) tests which are known as common posture transitions in daily life monitoring. Fifteen features from time, frequency and nonlinear domains were extracted from both electrocardiography and PPG signals. Paired t-tests and Wilcoxon signed-rank tests examined the difference between the extracted HRV and PRV features during supine, transition and standing phases separately. One feature showed significant difference in the supine phase, and this discrepancy increased to four in the transition and standing phases. These findings suggested that PRV is different from HRV in the STS tests, despite the fact that both metrics can reflect the sympathetic activation triggered by the posture changes.
Towards THz-based Obstacle Sensing: A Generative Radio Environment Awareness Framework
Authors: Tianyu Hu, Yunhang Xie, Shuai Wang, Boyu Ning, Lingxiang Li, Zhi Chen
Date: 2025-02-11 00:00:00
Tasks: Generative Adversarial Network

Obstacle sensing is essential for terahertz (THz) communication since the subsequent beam management can avoid THz signals blocked by the obstacles. In parallel, radio environment, which can be manifested by channel knowledge such as the distribution of received signal strength (RSS), reveals signal propagation situation and the corresponding obstacle information. However, the awareness of the radio environment for obstacle sensing is challenging in practice, as the sparsely deployed THz sensors can acquire only little a priori knowledge with their RSS measurements. Therefore, we formulate in this paper a radio environment awareness problem, which for the first time considers a probability distribution of obstacle attributes. To solve such a problem, we propose a THz-based generative radio environment awareness framework, in which obstacle information is obtained directly from the aware radio environment. We also propose a novel generative model based on conditional generative adversarial network (CGAN), where U-net and the objective function of the problem are introduced to enable accurate awareness of RSS distribution. Simulation results show that the proposed framework can improve the awareness of the radio environment, and thus achieve superior sensing performance in terms of average precision regarding obstacles' shape and location.
Comprehensive Review of Deep Unfolding Techniques for Next-Generation Wireless Communication Systems
Authors: Sukanya Deka, Kuntal Deka, Nhan Thanh Nguyen, Sanjeev Sharma, Vimal Bhatia, Nandana Rajatheva
Date: 2025-02-09 00:00:00

The application of machine learning in wireless communications has been extensively explored, with deep unfolding emerging as a powerful model-based technique. Deep unfolding enhances interpretability by transforming complex iterative algorithms into structured layers of deep neural networks (DNNs). This approach seamlessly integrates domain knowledge with deep learning (DL), leveraging the strengths of both methods to simplify complex signal processing tasks in communication systems. To provide a solid foundation, we first present a brief overview of DL and deep unfolding. We then explore the applications of deep unfolding in key areas, including signal detection, channel estimation, beamforming design, decoding for error-correcting codes, sensing and communication, power allocation, and security. Each section focuses on a specific task, highlighting its significance in emerging 6G technologies and reviewing recent advancements in deep unfolding-based solutions. Finally, we discuss the challenges associated with developing deep unfolding techniques and propose potential improvements to enhance their applicability across diverse wireless communication scenarios.
Smell of Source: Learning-Based Odor Source Localization with Molecular Communication
Authors: Ayse Sila Okcu, Ozgur B. Akan
Date: 2025-02-10 00:00:00
Tasks: Computational Efficiency, Disaster Response, Reinforcement Learning (RL)

Odor source localization is a fundamental challenge in molecular communication, environmental monitoring, disaster response, industrial safety, and robotics. In this study, we investigate three major approaches: Bayesian filtering, machine learning (ML) models, and physics-informed neural networks (PINNs) with the aim of odor source localization in a single-source, single-molecule case. By considering the source-sensor architecture as a transmitter-receiver model we explore source localization under the scope of molecular communication. Synthetic datasets are generated using a 2D advection-diffusion PDE solver to evaluate each method under varying conditions, including sensor noise and sparse measurements. Our experiments demonstrate that \textbf{Physics-Informed Neural Networks (PINNs)} achieve the lowest localization error of \(\mathbf{0.89 \times 10^{-6}}\) m, outperforming \textbf{machine learning (ML) inversion} (\(\mathbf{1.48 \times 10^{-6}}\) m) and \textbf{Kalman filtering} (\(\mathbf{1.62 \times 10^{-6}}\) m). The \textbf{reinforcement learning (RL)} approach, while achieving a localization error of \(\mathbf{3.01 \times 10^{-6}}\) m, offers an inference time of \(\mathbf{0.147}\) s, highlighting the trade-off between accuracy and computational efficiency among different methodologies.
Bayesian Beamforming for Integrated Sensing and Communication Systems
Authors: Zongyao Zhao, Zhenyu Liu, Wei Dai, Xinke Tang, Xiao-Ping Zhang, Yuhan Dong
Date: 2025-02-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

The uncertainty of the sensing target brings great challenge to the beamforming design of the integrated sensing and communication (ISAC) system. To address this issue, we model the scattering coefficient and azimuth angle of the target as random variables and introduce a novel metric, expected detection probability (EPd), to quantify the average detection performance from a Bayesian perspective. Furthermore, we design a Bayesian beamforming scheme to optimize the expected detection probability under the limited power budget and communication performance constraints. A successive convex approximation and semidefinite relaxation-based (SCA-SDR) algorithm is developed for the complicated non-convex optimization problem corresponding to the beamforming scheme. Simulation results show that the proposed scheme outperforms other benchmarks and exhibits robust detection performance when parameters of the target are unknown and random.
Electromagnetic Channel Statistics for Continuous-Aperture Array (CAPA) Systems
Authors: Chongjun Ouyang, Boqun Zhao, Xingqi Zhang, Yuanwei Liu
Date: 2025-02-10 00:00:00

The channel statistics of a continuous-aperture array (CAPA)-based channel are analyzed using its continuous electromagnetic (EM) properties. The received signal-to-noise ratio (SNR) is discussed under isotropic scattering conditions. Using Landau's theorem, the eigenvalues of the autocorrelation of the EM fading channel are shown to exhibit a step-like behavior. Building on this, closed-form expressions for the probability distribution of the SNR and the average channel capacity are derived. Numerical results are provided to validate the accuracy of the derivations.
Optical RIS-enabled Multiple Access Communications
Authors: Georgios D. Chondrogiannis, Athanasios P. Chrysologou, Alexandros-Apostolos A. Boulogeorgos, Nestor D. Chatzidiamantis, Harald Haas
Date: 2025-02-10 00:00:00

In this paper, we identify optical reconfigurable intelligent surfaces (ORISs) as key enablers of next-generation free-space optical (FSO) multiple access systems. By leveraging their beam steering and beam splitting capabilities, ORISs are able to effectively address line-of-sight (LoS) constraints, while enabling multi-user connectivity. We consider an ORIS-assisted non-orthogonal multiple access (NOMA) system model consisting of a single transmitter (Tx) and two receivers (Rxs). We derive novel analytical expressions to characterize the statistical particularities of the Tx-ORIS-Rx communication channel. Building upon the aforementioned expressions, we investigate the outage performance of the Rxs by deriving exact analytical expressions for the outage probability (OP) of each Rx. To provide deeper insights into the impact of various system parameters and physical conditions on the outage performance of each Rx, we conduct a high signal-to-noise ratio (SNR) analysis, that returns asymptotic expressions for the Rxs OPs at the high-SNR regime. Monte Carlo simulations validate the analysis, demonstrate the effectiveness of ORIS-enabled NOMA under a variety of configurations and physical scenarios, and showcase its superiority over its orthogonal-based counterpart.
Estimation of Food Intake Quantity Using Inertial Signals from Smartwatches
Authors: Ioannis Levi, Konstantinos Kyritsis, Vasileios Papapanagiotou, Georgios Tsakiridis, Anastasios Delopoulos
Date: 2025-02-10 00:00:00

Accurate monitoring of eating behavior is crucial for managing obesity and eating disorders such as bulimia nervosa. At the same time, existing methods rely on multiple and/or specialized sensors, greatly harming adherence and ultimately, the quality and continuity of data. This paper introduces a novel approach for estimating the weight of a bite, from a commercial smartwatch. Our publicly-available dataset contains smartwatch inertial data from ten participants, with manually annotated start and end times of each bite along with their corresponding weights from a smart scale, under semi-controlled conditions. The proposed method combines extracted behavioral features such as the time required to load the utensil with food, with statistical features of inertial signals, that serve as input to a Support Vector Regression model to estimate bite weights. Under a leave-one-subject-out cross-validation scheme, our approach achieves a mean absolute error (MAE) of 3.99 grams per bite. To contextualize this performance, we introduce the improvement metric, that measures the relative MAE difference compared to a baseline model. Our method demonstrates a 17.41% improvement, while the adapted state-of-the art method shows a -28.89% performance against that same baseline. The results presented in this work establish the feasibility of extracting meaningful bite weight estimates from commercial smartwatch inertial sensors alone, laying the groundwork for future accessible, non-invasive dietary monitoring systems.
Retrieving Filter Spectra in CNN for Explainable Sleep Stage Classification
Authors: Stephan Goerttler, Yucheng Wang, Emadeldeen Eldele, Fei He, Min Wu
Date: 2025-02-10 00:00:00
Tasks: EEG

Despite significant advances in deep learning-based sleep stage classification, the clinical adoption of automatic classification models remains slow. One key challenge is the lack of explainability, as many models function as black boxes with millions of parameters. In response, recent work has increasingly focussed on enhancing model explainability. This study contributes to these efforts by globally explaining spectral processing of individual EEG channels. Specifically, we introduce a method to retrieve the filter spectrum of low-level convolutional feature extraction and compare it with the classification-relevant spectral information in the data. We evaluate our approach on the MSA-CNN model using the ISRUC-S3 and Sleep-EDF-20 datasets. Our findings show that spectral processing plays a significant role in the lower frequency bands. In addition, comparing the correlation between filter spectrum and data-based spectral information with univariate performance indicates that the model naturally prioritises the most informative channels in a multimodal setting. We specify how these insights can be leveraged to enhance model performance. The code for the filter spectrum retrieval and its analysis is available at https://github.com/sgoerttler/MSA-CNN.
A Novel Phenomenological Model of Equalization-enhanced Phase Noise
Authors: Benedikt Geiger, Fred Buchali, Vahid Aref, Laurent Schmalen
Date: 2025-02-10 00:00:00

We show that equalization-enhanced phase noise manifests as a time-varying, frequency-dependent phase error, which can be modeled and reversed by a time-varying all-pass finite impulse response filter.
A Novel Energy-aware Design for a D2D Underlaid UAV-Aided Cognitive Radio Network
Authors: Hossein Mohammadi Firouzjaei, Javad Zeraatkar Moghaddam, Mehrdad Ardebilipour
Date: 2025-02-10 00:00:00
Tasks: Disaster Response

This paper investigates the effectiveness of a free disaster response network (free-DRN) and an energy harvesting enabled DRN (EH-enabled DRN) enhanced by cognitive radio (CR) technology and unmanned aerial vehicles (UAVs). In the EH-enabled DRN scenario, where device-to-device (D2D) communication harvests energy from cellular users, significant improvements are demonstrated in terms of energy efficiency (EE) and communication rate compared to the free-DRN approach. The impact of user density and UAV specifications on network performance is analyzed, addressing the challenge of optimizing the duration of energy harvesting for both cellular users and D2D devices in the EH-enabled DRN scenario. Additionally, simulation results reveal an optimal UAV height that ensures efficient network operation for varying densities of D2D devices. Overall, numerical and simulation findings highlight the superior performance of the EH-enabled DRN approach, showcasing the positive effects of enabling D2D links and improved EE. Notably, reducing energy harvesting duration and cellular user density can further enhance the EE of the DRN by up to 3dB.
UAV-Enabled IoT Networks: A SWIPT Energy Harvesting Architecture with Relay Support for Disaster Response
Authors: Hossein Mohammadi Firouzjaei, Javad Zeraatkar Moghaddam, Mehrdad Ardebilipour
Date: 2025-02-10 00:00:00
Tasks: Disaster Response

Due to the wide application of unmanned aerial vehicles (UAVs) as relays to establish Disaster Response Networks (DRNs), an effective model of energy harvesting (EH) and energy consumption for the UAV-aided Disaster Response Network (DRN) is rising to be a challenging issue. This is mainly manifest in Internet of Things (IoT) scenarios where multiple users are looking to communicate with the UAV. In this paper, the possibility of connecting an UAV with several users is investigated where the UAV as a relay receives data from a DRN and delivers to another network considering two IoT scenarios. The first scenario represents a conventional method with limited UAV energy where low communication rates and inadequate service coverage for all users are challenges. But in the second scenario, a Simultaneous Wireless Information and Power Transmission (SWIPT) technique is used to serve users. Considering potential limitations in transmission energy of users within disaster networks, the SWIPT technique is applied to maximize energy acquisition by the UAV, leading to improve the efficiency of the investigated scenario. Finally, the required energy of the UAV to serve the largest number of users in the shortest possible time is clarified. Furthermore, by Considering the relationship between energy and UAV flight time and defining the UAV flight time optimization problem, optimal network parameters are obtained. Simulation results show the effectiveness of the proposed scenario.
Weighted-Sum Energy Efficiency Maximization in User-Centric Uplink Cell-Free Massive MIMO
Authors: Donghwi Kim, Liesbet Van der Perre, Wan Choi
Date: 2025-02-10 00:00:00
Tasks: CPU

This paper introduces the weighted-sum energy efficiency (WSEE) as an advanced performance metric designed to represent the uplink energy efficiency (EE) of individual user equipment (UE) in a user-centric Cell-Free massive MIMO (CF-mMIMO) system more accurately. In this realistic user-centric CF-mMIMO context, each UE may exhibit distinct characteristics, such as maximum transmit power limits or specific minimum data rate requirements. By computing the EE of each UE independently and adjusting the weights accordingly, the system can accommodate these unique attributes, thus promoting energy-efficient operation. The uplink WSEE is formulated as a multiple-ratio fractional programming (FP) problem, representing a weighted sum of the EE of individual UEs, which depends on each UE's transmit power and the combining vector at the CPU. To effectively maximize WSEE, we present optimization algorithms that utilize the Dinkelbach transform and the quadratic transform (QT). Applying the QT twice consecutively yields significant performance gains in terms of WSEE. This framework establishes a foundation for developing operational strategies tailored to specific system requirements.
Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition
Authors: Chenyu Liu, Jinshui Hu, BaoCai Yin, Jia Pan, Bing Yin, Jun Du, Qingfeng Liu
Date: 2025-02-10 00:00:00
Tasks: Decoder, Handwritten Text Recognition

Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attention-based recognition decoder. However, these approaches face several drawbacks: 1) single encoders typically focus on either local trajectories or visual regions, lacking the ability to dynamically capture relevant global features in challenging cases; 2) multi-stream encoders, while more comprehensive, suffer from complex structures and increased inference costs. To tackle this, we propose a Collaborative learning-based OLHTR framework, called Col-OLHTR, that learns multimodal features during training while maintaining a single-stream inference process. Col-OLHTR consists of a trajectory encoder, a Point-to-Spatial Alignment (P2SA) module, and an attention-based decoder. The P2SA module is designed to learn image-level spatial features through trajectory-encoded features and 2D rotary position embeddings. During training, an additional image-stream encoder-decoder is collaboratively trained to provide supervision for P2SA features. At inference, the extra streams are discarded, and only the P2SA module is used and merged before the decoder, simplifying the process while preserving high performance. Extensive experimental results on several OLHTR benchmarks demonstrate the state-of-the-art (SOTA) performance, proving the effectiveness and robustness of our design.
Geolocation with Large LEO Constellations: Insights from Fisher Information
Authors: Don-Roberts Emenonye, Harpreet S. Dhillon, R. Michael Buehrer
Date: 2025-02-10 00:00:00

Interest in the use of the low earth orbit (LEO) in space - from $160 \text{ km}$ to $2000 \text{ km}$ - has skyrocketed; this is evident by the fact that National Aeronautics and Space Administration (NASA) has partnered with various commercial platforms like Axiom Space, Blue Origin, SpaceX, Sierra Space, Starlab Space, ThinkOrbital, and Vast Space to deploy satellites. %and platforms like Northrop Grumman and Boeing to transport cargo and crew. The most apparent advantage of satellites in LEO over satellites in Geostationary (GEO) and medium earth orbit (MEO) is their closeness to the earth; hence, signals from LEOs encounter lower propagation losses and reduced propagation delay, opening up the possibility of using these LEO satellites for localization. This article reviews the existing signal processing algorithms for localization using LEO satellites, introduces the basics of estimation theory, connects estimation theory to model identifiability with Fisher Information Matrix (FIM), and with the FIM, provides conditions that allow for $9$D localization of a terrestrial receiver using signals from multiple LEOs (unsynchronized in time and frequency) across multiple time slots and multiple receive antennas. We also compare the structure of the information available in LEO satellites with the structure of the information available in the Global Positioning System (GPS).
Beam Training for Pinching-Antenna Systems (PASS)
Authors: Suyu Lv, Yuanwei Liu, Zhiguo Ding
Date: 2025-02-09 00:00:00

This article investigates the beam training design problems for pinching-antenna systems (PASS), where single-waveguide-single-user (SWSU), single-waveguide-multi-user (SWMU) and multi-waveguide-multi-user (MWMU) scenarios are considered. For SWSU-PASS, we design a scalable codebook, based on which we propose a three-stage beam training (3SBT) scheme. Specifically, 1) firstly, the 3SBT scheme utilizes one activated pinching antenna to obtain a coarse one-dimensional location at the first stage; 2) secondly, it achieves further phase matching with an increased number of activated antennas at the second stage; 3) finally, it realizes precise beam alignment through an exhaustive search at the third stage. For SWMU-PASS, based on the scalable codebook design, we propose an improved 3SBT scheme to support non-orthogonal multiple access (NOMA) transmission. For MWMU-PASS, we first present a generalized expression of the received signal based on the partially-connected hybrid beamforming structure. Furthermore, we introduce an increased-dimensional scalable codebook design, based on which an increased-dimensional 3SBT scheme is proposed. Numerical results reveal that: i) the proposed beam training scheme can significantly reduce the training overhead compared to the two-dimensional exhaustive search, while maintaining reasonable rate performance; ii) compared to fixed-location pinching antennas and conventional array antennas, the proposed dynamic pinching antennas yield better flexibility and improved performance.