pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Modal Large Models Based Beam Prediction: An Example Empowered by DeepSeek
Authors: Yizhu Zhao, Li Yu, Lianzheng Shi, Jianhua Zhang, Guangyi Liu
Date: 2025-06-06 00:00:00
Tasks: Beam Prediction, Prediction

Beam prediction is an effective approach to reduce training overhead in massive multiple-input multiple-output (MIMO) systems. However, existing beam prediction models still exhibit limited generalization ability in diverse scenarios, which remains a critical challenge. In this paper, we propose MLM-BP, a beam prediction framework based on the multi-modal large model released by DeepSeek, with full consideration of multi-modal environmental information. Specifically, the distribution of scatterers that impact the optimal beam is captured by the sensing devices. Then positions are tokenized to generate text-based representations, and multi-view images are processed by an image encoder, which is fine-tuned with low-rank adaptation (LoRA), to extract environmental embeddings. Finally, these embeddings are fed into the large model, and an output projection module is designed to determine the optimal beam index. Simulation results show that MLM-BP achieves 98.1% Top-1 accuracy on the simulation dataset. Additionally, it demonstrates few-shot generalization on a real-world dataset, achieving 72.7% Top-1 accuracy and 92.4% Top-3 accuracy with only 30% of the dataset, outperforming the existing small models by over 15%.
DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series
Authors: Adrien Petralia, Paul Boniol, Philippe Charpentier, Themis Palpanas
Date: 2025-06-06 00:00:00
Tasks: Time Series

In recent years, electricity suppliers have installed millions of smart meters worldwide to improve the management of the smart grid system. These meters collect a large amount of electrical consumption data to produce valuable information to help consumers reduce their electricity footprint. However, having non-expert users (e.g., consumers or sales advisors) understand these data and derive usage patterns for different appliances has become a significant challenge for electricity suppliers because these data record the aggregated behavior of all appliances. At the same time, ground-truth labels (which could train appliance detection and localization models) are expensive to collect and extremely scarce in practice. This paper introduces DeviceScope, an interactive tool designed to facilitate understanding smart meter data by detecting and localizing individual appliance patterns within a given time period. Our system is based on CamAL (Class Activation Map-based Appliance Localization), a novel weakly supervised approach for appliance localization that only requires the knowledge of the existence of an appliance in a household to be trained. This paper appeared in ICDE 2025.
On Level Crossings and Fade Durations in von Mises-Fisher Scattering Channels
Authors: Kenan Turbic, Slawomir Stanczak
Date: 2025-06-06 00:00:00

This paper investigates the second-order statistics of multipath fading channels with von Mises-Fisher (vMF) distributed scatters. Simple closed-form expressions for the mean Doppler shift and Doppler spread are derived as the key spectral moments that capture the impact of mobility and scattering characteristics on level crossings and fade durations. These expressions are then used to analyze the influence of vMF parameters on the Level-Crossing Rate (LCR) The results show that isotropic scattering yields the highest LCR, while fading dynamics reduce with the decreasing angular spread of scatterers. Moreover, obile antenna motion parallel to the mean scattering direction results in a lower LCR than the perpendicular motion, with the difference between the two cases increasing with the higher concentration of scatterers.
NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity
Authors: Adrien Petralia, Philippe Charpentier, Youssef Kadhi, Themis Palpanas
Date: 2025-06-06 00:00:00
Tasks: Non-Intrusive Load Monitoring

Millions of smart meters have been deployed worldwide, collecting the total power consumed by individual households. Based on these data, electricity suppliers offer their clients energy monitoring solutions to provide feedback on the consumption of their individual appliances. Historically, such estimates have relied on statistical methods that use coarse-grained total monthly consumption and static customer data, such as appliance ownership. Non-Intrusive Load Monitoring (NILM) is the problem of disaggregating a household's collected total power consumption to retrieve the consumed power for individual appliances. Current state-of-the-art (SotA) solutions for NILM are based on deep-learning (DL) and operate on subsequences of an entire household consumption reading. However, the non-stationary nature of real-world smart meter data leads to a drift in the data distribution within each segmented window, which significantly affects model performance. This paper introduces NILMFormer, a Transformer-based architecture that incorporates a new subsequence stationarization/de-stationarization scheme to mitigate the distribution drift and that uses a novel positional encoding that relies only on the subsequence's timestamp information. Experiments with 4 real-world datasets show that NILMFormer significantly outperforms the SotA approaches. Our solution has been deployed as the backbone algorithm for EDF's (Electricit\'e De France) consumption monitoring service, delivering detailed insights to millions of customers about their individual appliances' power consumption. This paper appeared in KDD 2025.
TinyML-Based Adaptive Pulse Shaping for Edge Intelligence in IoT/IIoT
Authors: Afan Ali
Date: 2025-06-06 00:00:00

Edge intelligence in IoT and IIoT demands lightweight algorithms for data processing on resource-constrained devices. This paper introduces a novel adaptive pulse shape filter based on TinyML for PAPR and SER optimization on edge devices used in uplink IoT communication. Implemented on IoT nodes such as sensors, our pruned neural network provides up to 2 dB PAPR saving over root-raised-cosine (RRC) filters. Mass simulations validate its efficacy in DFT-s-OFDM systems and offer an energy-efficient and scalable solution for IoT/IIoT use cases such as smart factories and rural connectivity.
UAV-Based Remote Sensing of Soil Moisture Across Diverse Land Covers: Validation and Bayesian Uncertainty Characterization
Authors: Runze Zhang, Ishfaq Aziz, Derek Houtz, Yuxiang Zhao, Trent W. Ford, Adam C. Watts, Mohamad Alipour
Date: 2025-06-05 00:00:00
Tasks: Bayesian Inference

High-resolution soil moisture (SM) observations are critical for agricultural monitoring, forestry management, and hazard prediction, yet current satellite passive microwave missions cannot directly provide retrievals at tens-of-meter spatial scales. Unmanned aerial vehicle (UAV) mounted microwave radiometry presents a promising alternative, but most evaluations to date have focused on agricultural settings, with limited exploration across other land covers and few efforts to quantify retrieval uncertainty. This study addresses both gaps by evaluating SM retrievals from a drone-based Portable L-band Radiometer (PoLRa) across shrubland, bare soil, and forest strips in Central Illinois, U.S., using a 10-day field campaign in 2024. Controlled UAV flights at altitudes of 10 m, 20 m, and 30 m were performed to generate brightness temperatures (TB) at spatial resolutions of 7 m, 14 m, and 21 m. SM retrievals were carried out using multiple tau-omega-based algorithms, including the single channel algorithm (SCA), dual channel algorithm (DCA), and multi-temporal dual channel algorithm (MTDCA). A Bayesian inference framework was then applied to provide probabilistic uncertainty characterization for both SM and vegetation optical depth (VOD). Results show that the gridded TB distributions consistently capture dry-wet gradients associated with vegetation density variations, and spatial correlations between polarized observations are largely maintained across scales. Validation against in situ measurements indicates that PoLRa derived SM retrievals from the SCAV and MTDCA algorithms achieve unbiased root-mean-square errors (ubRMSE) generally below 0.04 m3/m3 across different land covers. Bayesian posterior analyses confirm that reference SM values largely fall within the derived uncertainty intervals, with mean uncertainty ranges around 0.02 m3/m3 and 0.11 m3/m3 for SCA and DCA related retrievals.
Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs
Authors: Christiana Chamon, Abhijit Sarkar, A. Lynn Abbott
Date: 2025-06-05 00:00:00
Tasks: Anomaly Detection

Wearable and implantable healthcare sensors are pivotal for real-time patient monitoring but face critical challenges in power efficiency, data security, and signal noise. This paper introduces a novel platform that leverages hardware noise as a dual-purpose resource to enhance machine learning (ML) robustness and secure data via Physical Unclonable Functions (PUFs). By integrating noise-driven signal processing, PUFbased authentication, and ML-based anomaly detection, our system achieves secure, low-power monitoring for devices like ECG wearables. Simulations demonstrate that noise improves ML accuracy by 8% (92% for detecting premature ventricular contractions (PVCs) and atrial fibrillation (AF)), while PUFs provide 98% uniqueness for tamper-resistant security, all within a 50 uW power budget. This unified approach not only addresses power, security, and noise challenges but also enables scalable, intelligent sensing for telemedicine and IoT applications.
Pinching Antenna Systems versus Reconfigurable Intelligent Surfaces in mmWave
Authors: Mostafa Samy, Hayder Al-Hraishawi, Madyan Alsenwi, Abuzar B. M. Adam, Symeon Chatzinotas, Björn Otteresten
Date: 2025-06-05 00:00:00

Flexible and intelligent antenna designs, such as pinching antenna systems and reconfigurable intelligent surfaces (RIS), have gained extensive research attention due to their potential to enhance the wireless channels. This letter, for the first time, presents a comparative study between the emerging pinching antenna systems and RIS in millimeter wave (mmWave) bands. Our results reveal that RIS requires an extremely large number of elements (in the order of $10^4$) to outperform pinching antenna systems in terms of spectral efficiency, which severely impact the energy efficiency performance of RIS. Moreover, pinching antenna systems demonstrate greater robustness against hardware impairments and severe path loss typically encountered in high-frequency mmWave bands.
Massive MIMO with 1-Bit DACs: Data Detection for Quantized Linear Precoding with Dithering
Authors: Amin Radbord, Italo Atzeni, Antti Tölli
Date: 2025-06-05 00:00:00
Tasks: Quantization

To leverage high-frequency bands in 6G wireless systems and beyond, employing massive multiple-input multipleoutput (MIMO) arrays at the transmitter and/or receiver side is crucial. To mitigate the power consumption and hardware complexity across massive frequency bands and antenna arrays, a sacrifice in the resolution of the data converters will be inevitable. In this paper, we consider a point-to-point massive MIMO system with 1-bit digital-to-analog converters at the transmitter, where the linearly precoded signal is supplemented with dithering before the 1-bit quantization. For this system, we propose a new maximumlikelihood (ML) data detection method at the receiver by deriving the mean and covariance matrix of the received signal, where symbol-dependent linear minimum mean squared error estimation is utilized to efficiently linearize the transmitted signal. Numerical results show that the proposed ML method can provide gains of more than two orders of magnitude in terms of symbol error rate over conventional data detection based on soft estimation.
Development and Testing of a Low Cost Ultrasonic Leak Detector
Authors: Senol Gulgonul
Date: 2025-06-05 00:00:00

This study focuses on the development of an ultrasonic leak detection system utilizing the Arduino Nano 33 BLE Sense Rev2 board. The research aimed to create a compact and cost-effective solution for identifying leaks in high-pressure pipes. Algorithms were designed to enable lossless recording and processing of sound data captured by the onboard MEMS microphone. Key signal processing techniques, including the implementation of an IIR high-pass filter and RMS calculation, were employed to detect ultrasonic frequencies associated with leaks. The system was tested on a pressurized pipe setup, demonstrating its ability to accurately identify leaks. The results highlight the system's effectiveness, with its compact design and low cost making it suitable for a wide range of industrial applications. This research contributes a practical and accessible tool for leak detection, offering potential benefits in industrial applications.
Design of OTFS Signals with Pulse Shaping and Window Function for OTFS-Based Radar
Authors: Liangchen Sun, Yutaka Jitsumatsu
Date: 2025-06-05 00:00:00

We propose a pulse radar system that employs a generalized window function derived from the root raised cosine (RRC), which relaxes the conventional constraint that the window values are within the range [0, 1]. The proposed window allows both negative values and values exceeding 1, enabling greater flexibility in signal design. The system transmits orthogonal time frequency space (OTFS) signals intermittently, establishing a flexible input-output relationship that captures both fractional delays and Doppler shifts. By combining the generalized RRC window with a rectangular pulse, the resulting pilot signal achieves a sharp concentration in the ambiguity function over both the delay and Doppler domains. To enhance the estimation accuracy of fractional parameters, we apply frequency-domain interpolation based on the autocorrelation of the RRC window, which outperforms conventional linear interpolation by preserving the signal structure more effectively.
Restoration of contaminated data in an Intensity Mapping survey using deep neural networks
Authors: Lin-Cheng Li, Jia-Yu Lin, Yuan-Gen Wang, Lister Staveley-Smith
Date: 2025-06-05 00:00:00

21-cm Intensity Mapping (IM) is a promising approach to detecting information about the large-scale structure beyond the local universe. One of the biggest challenges for an IM observation is the foreground removal procedure. In this paper, we attempt to conduct the restoration of contaminated data in an IM experiment with a Deep Neural Network (DNN). To investigate the impact of such data restoration, we compare the root-mean-square (RMS) of data with and without restoration after foreground removal using polynomial fitting, singular value decomposition, and independent component analysis, respectively. We find that the DNN-based pipeline performs well in lowering the RMS level of data, especially for data with large contaminated fractions. Furthermore, we investigate the impact of the restoration on the large-scale 21-cm signal in the simulation generated by CRIME. Simulation results show that the angular power spectrum curves from data with restoration are closer to the real one. Our work demonstrates that the DNN-based data restoration approach significantly increases the signal-to-noise ratio compared with conventional ones, achieving excellent potential for IM observations.
Spectral Efficiency Maximization for mmWave MIMO-Aided Integrated Sensing and Communication Under Practical Constraints
Authors: Jitendra Singh, Anand Mehrotra, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo
Date: 2025-06-05 00:00:00
Tasks: Fairness, Integrated sensing and communication, ISAC

A hybrid transmit precoder (TPC) and receive combiner (RC) pair is conceived for millimeter wave (mmWave) multiple input multiple output (MIMO) integrated sensing and communication (ISAC) systems. The proposed design considers a practical mean squared error (MSE) constraint between the desired and the achieved beampatterns constructed for identifying radar targets (RTs). To achieve optimal performance, we formulate an optimization problem relying on sum spectral efficiency (SE) maximization of the communication users (CUs), while satisfying certain radar beampattern similarity (RBPS), total transmit power, and constant modulus constraints, where the latter are attributed to the hybrid mmWave MIMO architecture. Since the aforementioned problem is non-convex and intractable, a sequential approach is proposed wherein the TPCs are designed first, followed by the RCs. To deal with the non-convex MSE and constant modulus constraints in the TPC design problem, we propose a majorization and minimization (MM) based Riemannian conjugate gradient (RCG) method, which restricts the tolerable MSE of the beampattern to within a predefined limit. Moreover, the least squares and the zero-forcing methods are adopted for maximizing the sum-SE and for mitigating the multiuser interference (MUI), respectively. Furthermore, to design the RC at each CU, we propose a linear MM-based blind combiner (LMBC) scheme that does not rely on the knowledge of the TPC at the CUs and has a low complexity. To achieve user fairness, we further extend the proposed sequential approach for maximizing the geometric mean (GM) of the CU's rate. Simulation results are presented, which show the superior performance of the proposed hybrid TPC and RC in comparison to the state-of-the-art designs in the mmWave MIMO ISAC systems under consideration.
DAS-MAE: A self-supervised pre-training framework for universal and high-performance representation learning of distributed fiber-optic acoustic sensing
Authors: Junyi Duan, Jiageng Chen, Zuyuan He
Date: 2025-06-05 00:00:00
Tasks: Representation Learning

Distributed fiber-optic acoustic sensing (DAS) has emerged as a transformative approach for distributed vibration measurement with high spatial resolution and long measurement range while maintaining cost-efficiency. However, the two-dimensional spatial-temporal DAS signals present analytical challenges. The abstract signal morphology lacking intuitive physical correspondence complicates human interpretation, and its unique spatial-temporal coupling renders conventional image processing methods suboptimal. This study investigates spatial-temporal characteristics and proposes a self-supervised pre-training framework that learns signals' representations through a mask-reconstruction task. This framework is named the DAS Masked AutoEncoder (DAS-MAE). The DAS-MAE learns high-level representations (e.g., event class) without using labels. It achieves up to 1% error and 64.5% relative improvement (RI) over the semi-supervised baseline in few-shot classification tasks. In a practical external damage prevention application, DAS-MAE attains a 5.0% recognition error, marking a 75.7% RI over supervised training from scratch. These results demonstrate the high-performance and universal representations learned by the DAS-MAE framework, highlighting its potential as a foundation model for analyzing massive unlabeled DAS signals.
Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications
Authors: Charalampos Tsirmpas, Stasinos Konstantopoulos, Dimitris Andrikopoulos, Konstantina Kyriakouli, Panagiotis Fatouros
Date: 2025-06-04 00:00:00

Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting.
Identification of RIS-Assisted Paths for Wireless Integrated Sensing and Communication
Authors: Zeyu Huang, Stefan Schwarz, Bashar Tahir, Markus Rupp
Date: 2025-06-04 00:00:00
Tasks: Integrated sensing and communication

Distinguishing between reconfigurable intelligent surface (RIS) assisted paths and non-line-of-sight (NLOS) paths is a fundamental problem for RIS-assisted integrated sensing and communication. In this work, we propose a pattern alternation scheme for the RIS response that uses part of the RIS as a dynamic part to modulate the estimated channel power, which can considerably help the user equipments (UEs) to identify the RIS-assisted paths. Under such a dynamic setup, we formulate the detection framework for a single UE, where we develop a statistical model of the estimated channel power, allowing us to analytically evaluate the performance of the system. We investigate our method under two critical factors: the number of RIS elements allocated for the dynamic part and the allocation of RIS elements among different users. Simulation results verify the accuracy of our analysis.
MudiNet: Task-guided Disentangled Representation Learning for 5G Indoor Multipath-assisted Positioning
Authors: Ye Tian, Xueting Xu, Ao Peng
Date: 2025-06-04 00:00:00
Tasks: Representation Learning, Variational Inference

In the fifth-generation communication system (5G), multipath-assisted positioning (MAP) has emerged as a promising approach. With the enhancement of signal resolution, multipath component (MPC) are no longer regarded as noise but rather as valuable information that can contribute to positioning. However, existing research often treats reflective surfaces as ideal reflectors, while being powerless in the face of indistinguishable multipath caused by diffuse reflectors. This study approaches diffuse reflectors from the perspective of uncertainty, investigating the statistical distribution characteristics of indoor diffuse and specular reflectors. Based on these insights, a task-guided disentangled representation learning method leveraging multi-time channel impulse response (CIR) observations is designed to directly map CIRs to positions, while mitigating the adverse effects of components that contribute minimally to localization accuracy (e.g., diffuse multipath).In this semi-supervised learning framework, a global feature extraction architecture based on self-attention is proposed to capture location-independent wireless environmental information, while an MLP is employed to extract the time-varying features related to user equipment (UE) positions. Variational inference based on a latent variable model (LVM) is applied to separate independent features within the CIR, with position labels guiding the LVM to express components more beneficial for localization. Additionally, we provide a feasibility proof for the separability of diffuse and specular environmental features in CIRs. Simulation results demonstrate that the proposed method achieves higher localization accuracy compared to conventional search-based localization methods, with enhanced robustness against indistinguishable multipath from diffuse reflectors.