pwc-archive/papers-with-abstracts huggingface dataset:
SVD-Based Graph Fractional Fourier Transform on Directed Graphs and Its Application
Authors: Lu Li, Haiye Huo
Date: 2025-06-04 00:00:00
Tasks: Denoising

Graph fractional Fourier transform (GFRFT) is an extension of graph Fourier transform (GFT) that provides an additional fractional analysis tool for graph signal processing (GSP) by generalizing temporal-vertex domain Fourier analysis to fractional orders. In recent years, a large number of studies on GFRFT based on undirected graphs have emerged, but there are very few studies on directed graphs. Therefore, in this paper, one of our main contributions is to introduce two novel GFRFTs defined on Cartesian product graph of two directed graphs, by performing singular value decomposition on graph fractional Laplacian matrices. We prove that two proposed GFRFTs can effectively express spatial-temporal data sets on directed graphs with strong correlation. Moreover, we extend the theoretical results to a generalized Cartesian product graph, which is constructed by $m$ directed graphs. Finally, the denoising performance of our proposed two GFRFTs are testified through simulation by processing hourly temperature data sets collected from 32 weather stations in the Brest region of France.
An OTFS-based Random Access Scheme for GNSS Independent Operation in NTN
Authors: Marius Caus, Musbah Shaat
Date: 2025-06-04 00:00:00

This paper investigates the random access procedure for non-terrestrial networks operating without global navigation satellite system (GNSS) support. In such scenarios, positioning uncertainties can reach several kilometers, which directly impacts the open-loop compensation mechanisms employed by the user equipment. To ensure that the resulting time and carrier frequency offsets can be handled by the network, the robustness of the standardized random access signal design and detection scheme must be enhanced. To extend radio access capabilities, identical Zadoff Chu (ZC) sequences are concatenated and then modulated into the orthogonal time frequency space (OTFS) modulation. Thanks to the specific characteristics of the OTFS-based random access signal, the received sequences are coherently combined, thereby maximizing the desired signal strength. Additionally, the proposed preamble minimizes the overhead associated with the cyclic prefix (CP) transmission. Numerical evaluations in a regenerative low-Earth-orbit (LEO) satellite scenario show that, despite significant positioning errors, the proposed OTFS random access design attains comparable peak-to-average power ratio (PAPR) and missed detection probability (MDP) to OFDM-based solutions, while improving spectral confinement and reducing overhead. These results demonstrate that the proposed OTFS-based random access design offers a robust and spectrally efficient alternative to OFDM for GNSS-independent NTN access.
Graph Neural Networks for Resource Allocation in Multi-Channel Wireless Networks
Authors: Lili Chen, Changyang She, Jingge Zhu, Jamie Evans
Date: 2025-06-04 00:00:00
Tasks: Graph Neural Network

As the number of mobile devices continues to grow, interference has become a major bottleneck in improving data rates in wireless networks. Efficient joint channel and power allocation (JCPA) is crucial for managing interference. In this paper, we first propose an enhanced WMMSE (eWMMSE) algorithm to solve the JCPA problem in multi-channel wireless networks. To reduce the computational complexity of iterative optimization, we further introduce JCPGNN-M, a graph neural network-based solution that enables simultaneous multi-channel allocation for each user. We reformulate the problem as a Lagrangian function, which allows us to enforce the total power constraints systematically. Our solution involves combining this Lagrangian framework with GNNs and iteratively updating the Lagrange multipliers and resource allocation scheme. Unlike existing GNN-based methods that limit each user to a single channel, JCPGNN-M supports efficient spectrum reuse and scales well in dense network scenarios. Simulation results show that JCPGNN-M achieves better data rate compared to eWMMSE. Meanwhile, the inference time of JCPGNN-M is much lower than eWMMS, and it can generalize well to larger networks.
Nonlinear Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation with Hardware Impairments
Authors: Arttu Arjas, Italo Atzeni
Date: 2025-06-04 00:00:00
Tasks: Quantization

Accurate channel estimation is critical for realizing the performance gains of massive multiple-input multiple-output (MIMO) systems. Traditional approaches to channel estimation typically assume ideal receiver hardware and linear signal models. However, practical receivers suffer from impairments such as nonlinearities in the low-noise amplifiers and quantization errors, which invalidate standard model assumptions and degrade the estimation accuracy. In this work, we propose a nonlinear channel estimation framework that models the distortion function arising from hardware impairments using Gaussian process (GP) regression while leveraging the inherent sparsity of massive MIMO channels. First, we form a GP-based surrogate of the distortion function, employing pseudo-inputs to reduce the computational complexity. Then, we integrate the GP-based surrogate of the distortion function into newly developed enhanced sparse Bayesian learning (SBL) methods, enabling distortion-aware sparse channel estimation. Specifically, we propose two nonlinear SBL methods based on distinct optimization objectives, each offering a different trade-off between estimation accuracy and computational complexity. Numerical results demonstrate significant gains over the Bussgang linear minimum mean squared error estimator and linear SBL, particularly under strong distortion and at high signal-to-noise ratio.
Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
Authors: Hang Fan, Mingxuan Li, Jingshi Cui, Zuhan Zhang, Wencai Run, Dunnan Liu
Date: 2025-06-04 00:00:00
Tasks: Scheduling

The rapid growth of EVs and the subsequent increase in charging demand pose significant challenges for load grid scheduling and the operation of EV charging stations. Effectively harnessing the spatiotemporal correlations among EV charging stations to improve forecasting accuracy is complex. To tackle these challenges, we propose EV-LLM for EV charging loads based on LLMs in this paper. EV-LLM integrates the strengths of Graph Convolutional Networks (GCNs) in spatiotemporal feature extraction with the generalization capabilities of fine-tuned generative LLMs. Also, EV-LLM enables effective data mining and feature extraction across multimodal and multidimensional datasets, incorporating historical charging data, weather information, and relevant textual descriptions to enhance forecasting accuracy for multiple charging stations. We validate the effectiveness of EV-LLM by using charging data from 10 stations in California, demonstrating its superiority over the other traditional deep learning methods and potential to optimize load grid scheduling and support vehicle-to-grid interactions.
High-Speed Ultra-Energy-Efficient Memristor-Based Massive MIMO SIC Detector Circuit with Hybrid Analog-Digital Computing Architecture
Authors: Jia-Hui Bi, Shaoshi Yang, Sheng Chen, Ping Zhang
Date: 2025-06-04 00:00:00
Tasks: GPU

The emerging memristor crossbar array based computing circuits exhibit computing speeds and energy efficiency far surpassing those of traditional digital processors. This type of circuits can complete high-dimensional matrix operations in an extremely short time through analog computing, making it naturally applicable to linear detection and maximum likelihood detection in massive multiple-input multiple-output (MIMO) systems. However, the challenge of employing memristor crossbar arrays to efficiently implement other nonlinear detection algorithms, such as the successive interference cancellation (SIC) algorithm, remains unresolved. In this paper we propose a memristor-based circuit design for massive MIMO SIC detector. The proposed circuit comprises several judiciously designed analog matrix computing modules and hybrid analog-digital slicers, which enables the proposed circuit to perform the SIC algorithm with a hybrid analog-digital computing architecture. We show that the computing speed and the computational energy-efficiency of the proposed detector circuit are 43 times faster and 110 times higher, respectively, than those of a traditional 8-core digital signal processor (DSP), and also advantageous over the benchmark high-performance field programmable gate array (FPGA) and graphics processing unit (GPU).
Minimally Invasive Brain Computer Interfaces: Evaluating the Impact of Tissue Layers on Signal Quality of Sub-Scalp EEG
Authors: Timothy B Mahoney, Jingyang Liu, Huakun Xin, David B Grayden, Sam E John
Date: 2025-06-03 00:00:00
Tasks: Brain Computer Interface, EEG

Individuals with severe physical disabilities often experience diminished quality of life stemming from limited ability to engage with their surroundings. Brain-Computer Interface (BCI) technology aims to bridge this gap by enabling direct technology interaction. However, current BCI systems require invasive procedures, such as craniotomy or implantation of electrodes through blood vessels, posing significant risks to patients. Sub-scalp electroencephalography (EEG) offers a lower risk alternative. This study investigates the signal quality of sub-scalp EEG recordings from various depths in a sheep model, and compares results with other methods: ECoG and endovascular arrays. A computational model was also constructed to investigate the factors underlying variations in electrode performance. We demonstrate that peg electrodes placed within the sub-scalp space can achieve visual evoked potential signal-to-noise ratios (SNRs) approaching that of ECoG. Endovascular arrays exhibited SNR comparable to electrodes positioned on the periosteum. Furthermore, sub-scalp recordings captured high gamma neural activity, with maximum bandwidth ranging from 120 Hz to 180 Hz depending on electrode depth. These findings support the use of sub-scalp EEG for BCI applications, and provide valuable insights for future sub-scalp electrode design. This data lays the groundwork for human trials, ultimately paving the way for chronic, in-home BCIs that empower individuals with physical disabilities.
Interference-Aware Multiuser Hybrid Precoding for Coexistence with LEO Satellite Communication
Authors: Nima Razavi, Murat Bayraktar, Nuria Gonzalez Prelcic, Robert W. Heath Jr
Date: 2025-06-03 00:00:00

Interference from terrestrial networks can reduce the communication rate for low Earth orbit (LEO) satellites in the upper mid-band. To coexist in frequency, MIMO precoding can be used to reduce the signal that impinges on the LEO satellite. We present a beamforming algorithm designed for the hybrid architecture that incorporates a satellite interference penalty while optimizing the analog and digital precoders. Our algorithm optimizes the precoding at the base station (BS) within the set of precoders that null the interference to the satellite. Simulations demonstrate that our algorithm reduces the interference at the satellites and lowers the probability of violating prescribed LEO satellite protection thresholds, outperforming prior hybrid nulling algorithms. Results indicate that the algorithm maintains sum-rate within 3\% of the existing hybrid solutions, while effectively improving interference to noise power by 22.4 dB.
Sub-Scalp EEG for Sensorimotor Brain-Computer Interface
Authors: Timothy B Mahoney, David B Grayden, Sam E John
Date: 2025-06-03 00:00:00
Tasks: Brain Computer Interface, EEG

Objective: To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity. Approach: Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment. Main Results: We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models. Significance: These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.
Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding
Authors: Weiqing Xiao, Hao Huang, Chonghao Zhong, Yujie Lin, Nan Wang, Xiaoxue Chen, Zhaoxi Chen, Saining Zhang, Shuocheng Yang, Pierre Merriaux, Lei Lei, Hao Zhao
Date: 2025-06-03 00:00:00
Tasks: 3D Object Detection, Attribute, Autonomous Driving, object-detection, Object Detection, Semantic Segmentation

We present SA-Radar (Simulate Any Radar), a radar simulation approach that enables controllable and efficient generation of radar cubes conditioned on customizable radar attributes. Unlike prior generative or physics-based simulators, SA-Radar integrates both paradigms through a waveform-parameterized attribute embedding. We design ICFAR-Net, a 3D U-Net conditioned on radar attributes encoded via waveform parameters, which captures signal variations induced by different radar configurations. This formulation bypasses the need for detailed radar hardware specifications and allows efficient simulation of range-azimuth-Doppler (RAD) tensors across diverse sensor settings. We further construct a mixed real-simulated dataset with attribute annotations to robustly train the network. Extensive evaluations on multiple downstream tasks-including 2D/3D object detection and radar semantic segmentation-demonstrate that SA-Radar's simulated data is both realistic and effective, consistently improving model performance when used standalone or in combination with real data. Our framework also supports simulation in novel sensor viewpoints and edited scenes, showcasing its potential as a general-purpose radar data engine for autonomous driving applications. Code and additional materials are available at https://zhuxing0.github.io/projects/SA-Radar.
PoLAR: Polar-Decomposed Low-Rank Adapter Representation
Authors: Kai Lion, Liang Zhang, Bingcong Li, Niao He
Date: 2025-06-03 00:00:00
Tasks: Mathematical Problem-Solving, Riemannian optimization

We show that low-rank adaptation of large-scale models suffers from a low stable rank that is well below the linear algebraic rank of the subspace, degrading fine-tuning performance. To mitigate the underutilization of the allocated subspace, we propose PoLAR, a parameterization inspired by the polar decomposition that factorizes the low-rank update into two direction matrices constrained to Stiefel manifolds and an unconstrained scale matrix. Our theory shows that PoLAR yields an exponentially faster convergence rate on a canonical low-rank adaptation problem. Pairing the parameterization with Riemannian optimization leads to consistent gains on three different benchmarks testing general language understanding, commonsense reasoning, and mathematical problem solving with base model sizes ranging from 350M to 27B.
Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
Authors: Alexandre Hippert-Ferrer, Aude Sportisse, Amirhossein Javaheri, Mohammed Nabil El Korso, Daniel P. Palomar
Date: 2025-06-02 00:00:00
Tasks: Imputation, Missing Values

This tutorial aims to provide signal processing (SP) and machine learning (ML) practitioners with vital tools, in an accessible way, to answer the question: How to deal with missing data? There are many strategies to handle incomplete signals. In this paper, we propose to group these strategies based on three common tasks: i) missing-data imputation, ii) estimation with missing values and iii) prediction with missing values. We focus on methodological and experimental results through specific case studies on real-world applications. Promising and future research directions, including a better integration of informative missingness, are also discussed. We hope that the proposed conceptual framework and the presentation of recent missing-data problems related will encourage researchers of the SP and ML communities to develop original methods and to efficiently deal with new applications involving missing data.
Towards Generalizable Drowsiness Monitoring with Physiological Sensors: A Preliminary Study
Authors: Jiyao Wang, Suzan Ayas, Jiahao Zhang, Xiao Wen, Dengbo He, Birsen Donmez
Date: 2025-06-03 00:00:00
Tasks: Privacy Preserving

Accurately detecting drowsiness is vital to driving safety. Among all measures, physiological-signal-based drowsiness monitoring can be more privacy-preserving than a camera-based approach. However, conflicts exist regarding how physiological metrics are associated with different drowsiness labels across datasets. Thus, we analyzed key features from electrocardiograms (ECG), electrodermal activity (EDA), and respiratory (RESP) signals across four datasets, where different drowsiness inducers (such as fatigue and low arousal) and assessment methods (subjective vs. objective) were used. Binary logistic regression models were built to identify the physiological metrics that are associated with drowsiness. Findings indicate that distinct different drowsiness inducers can lead to different physiological responses, and objective assessments were more sensitive than subjective ones in detecting drowsiness. Further, the increased heart rate stability, reduced respiratory amplitude, and decreased tonic EDA are robustly associated with increased drowsiness. The results enhance understanding of drowsiness detection and can inform future generalizable monitoring designs.
Derivation of CRB and Refined SINR Expressions for OTFS-RSMA LEO ISAC Systems
Authors: Bruno Felipe Costa, Taufik Abrão
Date: 2025-06-03 00:00:00
Tasks: ISAC

This document provides detailed step-by-step derivations for the Cram\'er-Rao Bounds (CRB) for sensing parameters and the refined Signal-to-Interference-plus-Noise Ratio (SINR) expressions under imperfect Channel State Information (CSI) and imperfect Successive Interference Cancellation (SIC) for the Orthogonal Time Frequency Space (OTFS) Rate-Splitting Multiple Access (RSMA) framework presented in our main work "An Integrated OTFS-RSMA Framework for LEO Satellite ISAC: Modeling, Metrics, and Potential". These derivations support the analytical expressions and models in the broad main discussion.
Coordinated Multi-BS SSB Beam Design for Enhanced Initial Access Coverage
Authors: Hakime BarghiZanjani, Bikshapathi Gouda, Antti Tölli
Date: 2025-06-03 00:00:00

Ensuring strong synchronization signal block (SSB) coverage is essential for reliable user equipment (UE) connection during initial access. While techniques such as power boosting and network densification are commonly used, this work explores joint transmission (JT) of SSBs as an alternative to enhance coverage. Although JT is widely applied in data transmission, its use for SSBs has not been explored due to the lack of channel state information, which prevents coherent signal alignment across base stations (BSs). To address this, we propose a repetition-based JT strategy using a small set of predefined phase configurations at the BSs. This enables the UE to coherently combine multiple SSB receptions and achieve constructive gain regardless of its location. To reduce overhead, a limited number of joint beam configurations is selected to maximize the coverage. Simulation results under the line-of-sight conditions show up to 6 dB relative SNR gain with JT of SSBs using 4 BSs, compared to independent SSB transmission under the same resource budget. These results highlight the potential of JT to improve the coverage of SSBs during the initial access.
Online Bayesian system identification in multivariate autoregressive models via message passing
Authors: T. N. Nisslbeck, Wouter M. Kouw
Date: 2025-06-03 00:00:00

We propose a recursive Bayesian estimation procedure for multivariate autoregressive models with exogenous inputs based on message passing in a factor graph. Unlike recursive least-squares, our method produces full posterior distributions for both the autoregressive coefficients and noise precision. The uncertainties regarding these estimates propagate into the uncertainties on predictions for future system outputs, and support online model evidence calculations. We demonstrate convergence empirically on a synthetic autoregressive system and competitive performance on a double mass-spring-damper system.
Towards real-time assessment of infrasound event detection capability using deep learning-based transmission loss estimation
Authors: Alice Janela Cameijo, Alexis Le Pichon, Youcef Sklab, Souhila Arib, Quentin Brissaud, Sven peter Naesholm, Constantino Listowski, Samir Aknine
Date: 2025-06-03 00:00:00
Tasks: Event Detection

Accurate modeling of infrasound transmission loss is essential for evaluating the performance of the International Monitoring System, enabling the effective design and maintenance of infrasound stations to support compliance of the Comprehensive Nuclear-Test-Ban Treaty. State-of-the-art propagation modeling tools enable transmission loss to be finely simulated using atmospheric models. However, the computational cost prohibits the exploration of a large parameter space in operational monitoring applications. To address this, recent studies made use of a deep learning algorithm capable of making transmission loss predictions almost instantaneously. However, the use of nudged atmospheric models leads to an incomplete representation of the medium, and the absence of temperature as an input makes the algorithm incompatible with long range propagation. In this study, we address these limitations by using both wind and temperature fields as inputs to a neural network, simulated up to 130 km altitude and 4,000 km distance. We also optimize several aspects of the neural network architecture. We exploit convolutional and recurrent layers to capture spatially and range-dependent features embedded in realistic atmospheric models, improving the overall performance. The neural network reaches an average error of 4 dB compared to full parabolic equation simulations and provides epistemic and data-related uncertainty estimates. Its evaluation on the 2022 Hunga Tonga-Hunga Ha'apai volcanic eruption demonstrates its prediction capability using atmospheric conditions and frequencies not included in the training. This represents a significant step towards near real-time assessment of International Monitoring System detection thresholds of explosive sources.