pwc-archive/papers-with-abstracts huggingface dataset:
Refined Metrics, Sensing Limits, and Resource Allocation in OTFS-RSMA LEO ISAC
Authors: Bruno Felipe Costa, Taufik Abrão
Date: 2025-06-03 00:00:00
Tasks: Fairness, ISAC

This paper develops an integrated OTFS-RSMA framework employing advanced SP techniques tailored for this demanding environment. We derive refined communication performance metrics, specifically SINR expressions capturing the practical effects of ICSI and ISIC. Moreover, fundamental sensing limits are established via CRB derivation incorporating parameter-dependent echo gain, linking waveform SP properties to estimation accuracy. The resource allocation is formulated as a non-convex optimization problem aiming for Max-Min Fairness under constraints derived from these SP metrics. Illustrative results, obtained via GA optimization, crucially demonstrate that the proposed RSMA scheme uniquely enables the simultaneous satisfaction of stringent communication and sensing constraints metrics, a capability not achieved by conventional SDMA. Such results {highlight the efficacy of the integrated OTFS-RSMA precoding and optimization approach for designing robust and feasible LEO-ISAC systems. Index Terms -- ISAC, LEO, OTFS, RSMA, Channel Modeling, CRB, SINR, ICSI, ISIC, Resource Allocation, Max-Min Fairness, Delay-Doppler (DD) Processing, Satellite Communications.
Large Language Models Can Achieve Explainable and Training-Free One-shot HRRP ATR
Authors: Lingfeng Chen, Panhe Hu, Zhiliang Pan, Qi Liu, Zhen Liu
Date: 2025-06-03 00:00:00
Tasks: In-Context Learning

This letter introduces a pioneering, training-free and explainable framework for High-Resolution Range Profile (HRRP) automatic target recognition (ATR) utilizing large-scale pre-trained Large Language Models (LLMs). Diverging from conventional methods requiring extensive task-specific training or fine-tuning, our approach converts one-dimensional HRRP signals into textual scattering center representations. Prompts are designed to align LLMs' semantic space for ATR via few-shot in-context learning, effectively leveraging its vast pre-existing knowledge without any parameter update. We make our codes publicly available to foster research into LLMs for HRRP ATR.
On the Performance of Pinching-Antenna Systems (PASS) with Orthogonal and Non-Orthogonal Multiple Access
Authors: Yanyu Cheng, Chongjun Ouyang, Yuanwei Liu, George K. Karagiannidis
Date: 2025-06-03 00:00:00
Tasks: Diversity

This paper conducts a comprehensive performance analysis for pinching-antenna systems (PASS) under both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) transmission. Given the cost of waveguides, we consider a scenario where the waveguide is not deployed in all rooms, i.e., some users are beyond the line-of-sight (LoS) link service area of the PASS. Specifically, we consider a PASS where a pinching antenna in one room serves two users located in separate rooms. The wireless transmissions between the pinching antenna and the users are performed via LoS and non-line-of-sight (NLoS) links, respectively. Closed-form expressions for the outage probabilities (OPs) of the two users are derived for the considered system. Furthermore, asymptotic analyses in the high signal-to-noise ratio (SNR) regime are performed to reveal the achievable diversity orders. Numerical simulations validate the accuracy of the theoretical analysis and show that: 1) compared with conventional antenna systems (CASS), the OP of the LoS user in PASS is significantly reduced for both OMA and NOMA schemes in the middle SNR regime and approaches zero as the SNR increases; 2) since the diversity orders of the NLoS user in CASS and PASS are the same, the movement of the pinching antenna has no significant effect on the OP of the NLoS user for the OMA and NOMA scenarios.
Passive Multi-Target Visible Light Positioning Based on Multi-Camera Joint Optimization
Authors: Wenxuan Pan, Yang Yang, Dong Wei, Meng Zhang, Zhiyu Zhu
Date: 2025-06-03 00:00:00

Camera-based visible light positioning (VLP) has emerged as a promising indoor positioning technique. However, the need for dedicated LED infrastructure and on-target cameras in existing algorithms limits their scalability and increases deployment costs. To address these limitations, this letter proposes a passive VLP algorithm based on Multi-Camera Joint Optimization (MCJO). In the considered system, multiple pre-calibrated cameras mounted on the ceiling continuously capture images of positioning targets equipped with point light sources, and can simultaneously localize these targets at the server. In particular, the proposed MCJO comprises two stages: It first estimates target positions via linear least squares from multi-view projection rays; then refines these positions through nonlinear joint optimization to minimize the reprojection error. Simulation results show that MCJO can achieve millimeter-level accuracy, with an improvement of 19% over state-of-the-art algorithms. Experimental results further show that MCJO can achieve an average position error as low as 5.63 mm.
Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Models
Authors: Yuchen Liang, Renxiang Huang, Lifeng Lai, Ness Shroff, Yingbin Liang
Date: 2025-06-02 00:00:00
Tasks: Image Generation

Discrete state space diffusion models have shown significant advantages in applications involving discrete data, such as text and image generation. It has also been observed that their performance is highly sensitive to the choice of rate matrices, particularly between uniform and absorbing rate matrices. While empirical results suggest that absorbing rate matrices often yield better generation quality compared to uniform rate matrices, existing theoretical works have largely focused on the uniform rate matrices case. Notably, convergence guarantees and error analyses for absorbing diffusion models are still missing. In this work, we provide the first finite-time error bounds and convergence rate analysis for discrete diffusion models using absorbing rate matrices. We begin by deriving an upper bound on the KL divergence of the forward process, introducing a surrogate initialization distribution to address the challenge posed by the absorbing stationary distribution, which is a singleton and causes the KL divergence to be ill-defined. We then establish the first convergence guarantees for both the $\tau$-leaping and uniformization samplers under absorbing rate matrices, demonstrating improved rates over their counterparts using uniform rate matrices. Furthermore, under suitable assumptions, we provide convergence guarantees without early stopping. Our analysis introduces several new technical tools to address challenges unique to absorbing rate matrices. These include a Jensen-type argument for bounding forward process convergence, novel techniques for bounding absorbing score functions, and a non-divergent upper bound on the score near initialization that removes the need of early-stopping.
Sums of Mixed Independent Positive Random Variables: A Unified Framework
Authors: Fernando Darío Almeida García, Michel Daoud Yacoub, José Cândido Silveira Santos Filho
Date: 2025-06-02 00:00:00

This paper proposes a comprehensive and unprecedented framework that streamlines the derivation of exact, compact -- yet tractable -- solutions for the probability density function (PDF) and cumulative distribution function (CDF) of the sum of a broad spectrum of mixed independent positive random variables (RVs). To showcase the framework's potential and extensive applicability, we tackle the enduring challenge of obtaining these statistics for the sum of fading variates in an exact, manageable, and unified manner. Specifically, we derive novel, tractable expressions for the PDF and CDF of the sum of Gaussian-class and non-Gaussian-class fading distributions, thereby covering a plethora of conventional, generalized, and recently introduced fading models. The proposed framework accommodates independent and identically distributed (i.i.d.) sums, independent but not necessarily identically distributed (i.n.i.d.) sums, and mixed-type sums. Moreover, we introduce the strikingly novel $\alpha$-$\mu$ mixture distribution that unifies all Gaussian-class fading models.
Characterization of the Combined Effective Radiation Pattern of UAV-Mounted Antennas and Ground Station
Authors: Mushfiqur Rahman, Ismail Guvenc, Jason A. Abrahamson, Amitabh Mishra, Arupjyoti Bhuyan
Date: 2025-06-02 00:00:00

An Unmanned Aerial Vehicle (UAV)-based communication typically involves a link between a UAV-mounted antenna and a ground station. The radiation pattern of both antennas is influenced by nearby reflecting surfaces and scatterers, such as the UAV body and the ground. Experimentally characterizing the effective radiation patterns of both antennas is challenging, as the received power depends on their interaction. In this study, we learn a combined radiation pattern from experimental UAV flight data, assuming the UAV travels with a fixed orientation (constant yaw angle and zero pitch/roll). We validate the characterized radiation pattern by cross-referencing it with experiments involving different UAV trajectories, all conducted under identical ground station and UAV orientation conditions. Experimental results show that the learned combined radiation pattern reduces received power estimation error by up to 10 dB, compared to traditional anechoic chamber radiation patterns that neglect the effects of the UAV body and surrounding objects.
EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology
Authors: Mattson Ogg, Rahul Hingorani, Diego Luna, Griffin W. Milsap, William G. Coon, Clara A. Scholl
Date: 2025-06-02 00:00:00
Tasks: Brain Computer Interface, EEG, Motor Imagery

Brain computer interface (BCI) research, as well as increasing portions of the field of neuroscience, have found success deploying large-scale artificial intelligence (AI) pre-training methods in conjunction with vast public repositories of data. This approach of pre-training foundation models using label-free, self-supervised objectives offers the potential to learn robust representations of neurophysiology, potentially addressing longstanding challenges in neural decoding. However, to date, much of this work has focused explicitly on standard BCI benchmarks and tasks, which likely overlooks the multitude of features these powerful methods might learn about brain function as well as other electrophysiological information. We introduce a new method for self-supervised BCI foundation model pre-training for EEG inspired by a transformer-based approach adapted from the HuBERT framework originally developed for speech processing. Our pipeline is specifically focused on low-profile, real-time usage, involving minimally pre-processed data and just eight EEG channels on the scalp. We show that our foundation model learned a representation of EEG that supports standard BCI tasks (P300, motor imagery), but also that this model learns features of neural data related to individual variability, and other salient electrophysiological components (e.g., alpha rhythms). In addition to describing and evaluating a novel approach to pre-training BCI models and neural decoding, this work opens the aperture for what kind of tasks and use-cases might exist for neural data in concert with powerful AI methods.
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Authors: Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka, Gene Cheung
Date: 2025-06-02 00:00:00
Tasks: Graph Learning, Model Selection

Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map via a simple linear transform to ones in a corresponding positive graph Laplacian, thus enabling reuse of spectral filtering tools designed for positive graphs. We propose an efficient method to learn a balanced signed graph Laplacian directly from data. Specifically, extending a previous linear programming (LP) based sparse inverse covariance estimation method called CLIME, we formulate a new LP problem for each Laplacian column $i$, where the linear constraints restrict weight signs of edges stemming from node $i$, so that nodes of same / different polarities are connected by positive / negative edges. Towards optimal model selection, we derive a suitable CLIME parameter $\rho$ based on a combination of the Hannan-Quinn information criterion and a minimum feasibility criterion. We solve the LP problem efficiently by tailoring a sparse LP method based on ADMM. We theoretically prove local solution convergence of our proposed iterative algorithm. Extensive experimental results on synthetic and real-world datasets show that our balanced graph learning method outperforms competing methods and enables reuse of spectral filters, wavelets, and graph convolutional nets (GCN) constructed for positive graphs.
A New 5 bit/2D-symbol Modulation Format for Relative Intensity Noise-dominated IM-DD Systems
Authors: Felipe Villenas, Kaiquan Wu, Yunus Can Gültekin, Jamal Riani, Alex Alvarado
Date: 2025-06-02 00:00:00

We propose a novel 5-bit/2D-symbol modulation format based on PAM-6 optimized for IM-DD systems dominated by relative intensity noise. The proposed modulation scheme improves SNR by 0.94 dB compared to conventional PAM-6 and achieves near-optimal BER performance.
Local Ambiguity Shaping for Doppler-Resilient Sequences Under Spectral and PAPR Constraints
Authors: Shi He, Lingsheng Meng, Yao Ge, Yong Liang Guan, David González G., Zilong Liu
Date: 2025-06-02 00:00:00
Tasks: Integrated sensing and communication

This paper focuses on designing Doppler-resilient sequences with low local Ambiguity Function (AF) sidelobes, subject to certain spectral and Peak-to-Average Power Ratio (PAPR) constraints. To achieve this, we propose two distinctoptimization algorithms: (i) an Alternating Minimization (AM) algorithm for superior Weighted Peak Sidelobe Level (WPSL) minimization, and (ii) a low-complexity Augmented Lagrangian-assisted Majorization Minimization (ALaMM) algorithm with effective WPSL suppression. The proposed schemes hold great potential for sequence design in future 6G and integrated sensing and communication applications, supporting robust sensing under spectral coexistence constraints in high-mobility scenarios.
Deep learning methods for modeling infrasound transmission loss in the middle atmosphere
Authors: Alexis Le Pichon, Alice Janela Cameijo, Samir Aknine, Youcef Sklab, Souhila Arib, Quentin Brissaud, Sven peter Naesholm
Date: 2025-06-02 00:00:00

Accurate modeling of infrasound transmission losses (TLs) is essential to assess the performance of the global International Monitoring System infrasound network. Among existing propagation modeling tools, parabolic equation (PE) method enables TLs to be finely modeled, but its computational cost does not allow exploration of a large parameter space for operational monitoring applications. To reduce computation times, Brissaud et al. 2023 explored the potential of convolutional neural networks trained on a large set of regionally simulated wavefields (< 1000 km from the source) to predict TLs with negligible computation times compared to PE simulations. However, this method struggles in unfavorable initial wind conditions, especially at high frequencies, and causal issues with winds at large distances from the source affecting ground TLs close to the source. In this study, we have developed an optimized convolutional network designed to minimize prediction errors while predicting TLs from globally simulated combined temperature and wind fields spanning over propagation ranges of 4000 km. Our approach enhances the previously proposed one by implementing key optimizations that improve the overall architecture performance. The implemented model predicts TLs with an average error of 8.6 dB in the whole frequency band (0.1-3.2 Hz) and explored realistic atmospheric scenarios.
Unified Interference-Aware Water-Filling for QoS-Constrained Communication, Sensing, and JRC
Authors: Ahmed Naeem, Anastassia Gharib, Hüseyin Arslan
Date: 2025-06-02 00:00:00
Tasks: Joint Radar-Communication

Water-filling (WF) algorithms are pivotal in maximizing capacity and spectral efficiency in multiple-input and multiple-output (MIMO) systems. However, traditional WF approaches cater solely to communication requirements, neglecting the emerging heterogeneity of 6G, including sensing and joint radar-communication (JRC). As these diverse demands grow in importance and have different Quality of Service (QoS) constraints, traditional WF becomes inadequate. Therefore, in this paper, we propose a unified interference-aware and QoS-constrained WF algorithm for systems with communication, sensing, and JRC. The proposed algorithm enables power allocation for multi-user MIMO systems, effectively addressing interference and balancing the support for heterogeneous user requirements.
Enabling Scalable Distributed Beamforming via Networked LEO Satellites Towards 6G
Authors: Yuchen Zhang Seungnyun Kim, Tareq Y. Al-Naffouri
Date: 2025-06-02 00:00:00
Tasks: Scheduling

In this paper, we propose scalable distributed beamforming schemes over low Earth orbit (LEO) satellite networks that rely solely on statistical channel state information for downlink orthogonal frequency division multiplexing systems. We begin by introducing the system model and presenting a pragmatic yet effective analog beamformer and user-scheduling design. We then derive a closed-form lower bound on the ergodic sum rate, based on the hardening bound, for the digital beamformer design. Next, we formulate a per-satellite power-constrained sum-rate maximization problem, whose centralized solution, obtained via the weighted minimum mean squared error (WMMSE) framework, establishes performance limits and motivates decentralized strategies. We subsequently introduce two decentralized optimization schemes, based on approximating the hardening bound and decentralizing the WMMSE framework, for representative inter-satellite link topologies. In the Ring scheme, satellites update beamformers locally and exchange intermediate parameters sequentially. In the Star scheme, edge satellites update beamformers locally and in parallel, achieving consensus on intermediate parameters at a central satellite using a penalty-dual decomposition framework. Extensive simulations demonstrate that our distributed designs achieve near-centralized performance with superior scalability, substantially outperforming simple closed-form beamformers and single-satellite baselines in sum rate. Additionally, the delay-overhead trade-off between the two topologies is revealed.
From Turbulence to Tranquility: AI-Driven Low-Altitude Network
Authors: Kürşat Tekbıyık, Amir Hossein Fahim Raouf, İsmail Güvenç, Mingzhe Chen, Güneş Karabulut Kurt, Antoine Lesage-Landry
Date: 2025-06-02 00:00:00
Tasks: Decision Making, Management, Trajectory Planning

Low Altitude Economy (LAE) networks own transformative potential in urban mobility, emergency response, and aerial logistics. However, these networks face significant challenges in spectrum management, interference mitigation, and real-time coordination across dynamic and resource-constrained environments. After addressing these challenges, this study explores three core elements for enabling intelligent LAE networks as follows machine learning-based spectrum sensing and coexistence, artificial intelligence (AI)-optimized resource allocation and trajectory planning, and testbed-driven validation and standardization. We highlight how federated and reinforcement learning techniques support decentralized, adaptive decision-making under mobility and energy constraints. In addition, we discuss the role of real-world platforms such as AERPAW in bridging the gap between simulation and deployment and enabling iterative system refinement under realistic conditions. This study aims to provide a forward-looking roadmap toward developing efficient and interoperable AI-driven LAE ecosystems.
Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning
Authors: Thien Nhan Vo
Date: 2025-06-02 00:00:00
Tasks: Heart Rate Variability

This study addresses the classification of heartbeats from ECG signals through two distinct approaches: traditional machine learning utilizing hand-crafted features and deep learning via transformed images of ECG beats. The dataset underwent preprocessing steps, including downsampling, filtering, and normalization, to ensure consistency and relevance for subsequent analysis. In the first approach, features such as heart rate variability (HRV), mean, variance, and RR intervals were extracted to train various classifiers, including SVM, Random Forest, AdaBoost, LSTM, Bi-directional LSTM, and LightGBM. The second approach involved transforming ECG signals into images using Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plots (RP), with these images subsequently classified using CNN architectures like VGG and Inception. Experimental results demonstrate that the LightGBM model achieved the highest performance, with an accuracy of 99% and an F1 score of 0.94, outperforming the image-based CNN approach (F1 score of 0.85). Models such as SVM and AdaBoost yielded significantly lower scores, indicating limited suitability for this task. The findings underscore the superior ability of hand-crafted features to capture temporal and morphological variations in ECG signals compared to image-based representations of individual beats. Future investigations may benefit from incorporating multi-lead ECG signals and temporal dependencies across successive beats to enhance classification accuracy further.
Energy-Efficient Integrated Communication and Computation via Non-Terrestrial Networks with Uncertainty Awareness
Authors: Xiao Tang, Yudan Jiang, Ruonan Zhang, Qinghe Du, Jinxin Liu, Naijin Liu
Date: 2025-06-02 00:00:00

Non-terrestrial network (NTN)-based integrated communication and computation empowers various emerging applications with global coverage. Yet this vision is severely challenged by the energy issue given the limited energy supply of NTN nodes and the energy-consuming nature of communication and computation. In this paper, we investigate the energy-efficient integrated communication and computation for the ground node data through a NTN, incorporating an unmanned aerial vehicle (UAV) and a satellite. We jointly consider ground data offloading to the UAV, edge processing on the UAV, and the forwarding of results from UAV to satellite, where we particularly address the uncertainties of the UAV-satellite links due to the large distance and high dynamics therein. Accordingly, we propose to minimize the weighted energy consumption due to data offloading, UAV computation, UAV transmission, and UAV propulsion, in the presence of angular uncertainties under Gaussian distribution within the UAV-satellite channels. The formulated problem with probabilistic constraints due to uncertainties is converted into a deterministic form by exploiting the Bernstein-type inequality, which is then solved using a block coordinate descent framework with algorithm design. Simulation results are provided to demonstrate the performance superiority of our proposal in terms of energy sustainability, along with the robustness against uncertain non-terrestrial environments.