pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Platform Methane Plume Detection via Model and Domain Adaptation
Authors: Vassiliki Mancoridis, Brian Bue, Jake H. Lee, Andrew K. Thorpe, Daniel Cusworth, Alana Ayasse, Philip G. Brodrick, Riley Duren
Date: 2025-06-02 00:00:00
Tasks: Domain Adaptation, Image-to-Image Translation, Transfer Learning, Unsupervised Image-To-Image Translation

Prioritizing methane for near-term climate action is crucial due to its significant impact on global warming. Previous work used columnwise matched filter products from the airborne AVIRIS-NG imaging spectrometer to detect methane plume sources; convolutional neural networks (CNNs) discerned anthropogenic methane plumes from false positive enhancements. However, as an increasing number of remote sensing platforms are used for methane plume detection, there is a growing need to address cross-platform alignment. In this work, we describe model- and data-driven machine learning approaches that leverage airborne observations to improve spaceborne methane plume detection, reconciling the distributional shifts inherent with performing the same task across platforms. We develop a spaceborne methane plume classifier using data from the EMIT imaging spectroscopy mission. We refine classifiers trained on airborne imagery from AVIRIS-NG campaigns using transfer learning, outperforming the standalone spaceborne model. Finally, we use CycleGAN, an unsupervised image-to-image translation technique, to align the data distributions between airborne and spaceborne contexts. Translating spaceborne EMIT data to the airborne AVIRIS-NG domain using CycleGAN and applying airborne classifiers directly yields the best plume detection results. This methodology is useful not only for data simulation, but also for direct data alignment. Though demonstrated on the task of methane plume detection, our work more broadly demonstrates a data-driven approach to align related products obtained from distinct remote sensing instruments.
Development of Hardware-in-Loop Framework for Satellite Communication Self-Healing Networks
Authors: Sambrama, Venkata Srirama Rohit Kantheti, Liang C Chu, Erik Blasch, Shih-Chun Lin
Date: 2025-06-01 00:00:00

The use of Low Earth Orbit (LEO) satellites in the next generation (Next-G) communication systems has been gaining traction over the last few years due to their potential for providing global connectivity with low latency. Since they are the closest to the earth they come with their own set of disadvantages including high vulnerability to jamming and interference. To address these issues, this paper introduces a resilient, self-healing network designed to optimize signal quality under dynamic interference and adversarial conditions. The network leverages inter-satellite communication and an intelligent algorithm selection process, incorporating combining techniques like distributed-Maximal Ratio Combining (d-MRC), distributed-Linear Minimum Mean Squared Error Estimation (d-LMMSE), and Selection Combining (SC). These algorithms are selected to improve performance by adapting to changing network conditions. To evaluate the effectiveness of the proposed solution, we develop a software-defined radio (SDR)-based hardware testbed and perform detailed performance evaluations. Additionally, we present results from field tests conducted on the AERPAW testbed, which validate the proposed combining solutions in real-world scenarios. The results show that our approach makes LEO satellite networks more reliable and better able to handle interference, making them suitable for critical communications.
Wasserstein Distributionally Robust Adaptive Beamforming
Authors: Kiarash Hassas Irani, Sergiy A. Vorobyov, Yongwei Huang
Date: 2025-06-01 00:00:00

Distributionally robust optimization (DRO)-based robust adaptive beamforming (RAB) enables enhanced robustness against model uncertainties, such as steering vector mismatches and interference-plus-noise covariance matrix estimation errors. Existing DRO-based RAB methods primarily rely on uncertainty sets characterized by the first- and second-order moments. In this work, we propose a novel Wasserstein DRO-based beamformer, using the worst-case signal-to-interference-plus-noise ratio maximization formulation. The proposed method leverages the Wasserstein metric to define uncertainty sets, offering a data-driven characterization of uncertainty. We show that the choice of the Wasserstein cost function plays a crucial role in shaping the resulting formulation, with norm-based and Mahalanobis-like quadratic costs recovering classical norm-constrained and ellipsoidal robust beamforming models, respectively. This insight highlights the Wasserstein DRO framework as a unifying approach, bridging deterministic and distributionally robust beamforming methodologies.
LD-RPMNet: Near-Sensor Diagnosis for Railway Point Machines
Authors: Wei Li, Xiaochun Wu, Xiaoxi Hu, Yuxuan Zhang, Sebastian Bader, Yuhan Huang
Date: 2025-06-01 00:00:00
Tasks: Computational Efficiency, Diagnostic, Fault Diagnosis

Near-sensor diagnosis has become increasingly prevalent in industry. This study proposes a lightweight model named LD-RPMNet that integrates Transformers and Convolutional Neural Networks, leveraging both local and global feature extraction to optimize computational efficiency for a practical railway application. The LD-RPMNet introduces a Multi-scale Depthwise Separable Convolution (MDSC) module, which decomposes cross-channel convolutions into pointwise and depthwise convolutions while employing multi-scale kernels to enhance feature extraction. Meanwhile, a Broadcast Self-Attention (BSA) mechanism is incorporated to simplify complex matrix multiplications and improve computational efficiency. Experimental results based on collected sound signals during the operation of railway point machines demonstrate that the optimized model reduces parameter count and computational complexity by 50% while improving diagnostic accuracy by nearly 3%, ultimately achieving an accuracy of 98.86%. This demonstrates the possibility of near-sensor fault diagnosis applications in railway point machines.
Scalable Association of Users in CF-mMIMO: A Synergy of Communication, Sensing, and JCAS
Authors: Ahmed Naeem, Anastassia Gharib, El Mehdi Amhoud, Hüseyin Arslan
Date: 2025-06-01 00:00:00

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a key enabler for the sixth generation (6G) networks, offering unprecedented spectral efficiency and ubiquitous coverage. In CF-mMIMO systems, the association of user equipments (UEs) to access points (APs) is a critical challenge, as it directly impacts network scalability, interference management, and overall system performance. Conventional association methods primarily focus on optimizing communication performance. However, with the emergence of sensing and joint communication and sensing (JCAS) requirements, conventional approaches become insufficient. To address this challenge, we propose a scalable user association (SUA) scheme for CF-mMIMO networks, considering heterogeneous UE requirements. Designed to enhance the performance of both sensing and communication, the proposed SUA scheme aims to ensure network scalability. This is achieved by dynamically assigning APs to UEs based on their specific service requirements (communication, sensing, or JCAS), while considering link quality, interference mitigation, and network-related constraints. Specifically, the proposed SUA scheme employs AP masking, link prioritization, and an optimization-based association mechanism to select the most suitable APs for each UE. Simulations show that, compared to conventional CF-mMIMO methods, the proposed SUA scheme significantly reduces interference and computational runtime, while improving the symbol error rate for communication and the probability of detection for sensing.
A Group-Wise Narrow Beam Design for Uplink Channel Estimation in Hybrid Beamforming Systems
Authors: Yufan Zhou, Yongbo Xiao, An Liu
Date: 2025-06-01 00:00:00
Tasks: Bayesian Inference

In this paper, we consider uplink channel estimation for massive multi-input multi-output (MIMO) systems with partially connected hybrid beamforming (PC-HBF) structures. Existing beam design and channel estimation schemes are usually based on ideal assumptions and require transmitting pilots across multiple timeslots, making them unsuitable for practical PC-HBF systems. To overcome these drawbacks, we propose a novel beam design and a corresponding channel estimation algorithm to achieve accurate and real-time uplink channel estimation. Firstly, we introduce a group-wise narrow beam design in the vertical dimension to suppress interference from undesired angular components and improve vertical angle estimation accuracy,which divides the columns of the uniform planar array (UPA)into groups and the vertical angle interval into sub-intervals.In this way, each group is assigned with a narrow beam to cover one vertical angle sub-interval, and the set of narrow beams is designed based on the filter design theory. Secondly, we optimize the antenna grouping pattern using the Estimation of Distribution Algorithm (EDA), balancing interference suppression and resolution capability in the horizontal dimension, leading to a better horizontal angle estimation performance. Finally, we design a low-complexity group-wise subspace constrained variational Bayesian inference (GW-SC-VBI) algorithm to fully take advantage of the proposed beam design to achieve both low-complexity and high-accurate channel estimation. Simulation results demonstrate that the proposed scheme achieves notable performance gains over baseline methods.
Self-Supervised-ISAR-Net Enables Fast Sparse ISAR Imaging
Authors: Ziwen Wang, JianPing Wang, Pucheng Li, Yifan Wu, Zegang Ding
Date: 2025-06-01 00:00:00
Tasks: Image Reconstruction, Self-Supervised Learning

Numerous sparse inverse synthetic aperture radar (ISAR) imaging methods based on unfolded neural networks have been developed for high-quality image reconstruction with sparse measurements. However, their training typically requires paired ISAR images and echoes, which are often difficult to obtain. Meanwhile, one property can be observed that for a certain sparse measurement configuration of ISAR, when a target is rotated around its center of mass, only the image of the target undergoes the corresponding rotation after ISAR imaging, while the grating lobes do not follow this rotation and are solely determined by the sparse-sampling pattern. This property is mathematically termed as the equivariant property. Taking advantage of this property, an unfolded neural network for sparse ISAR imaging with self-supervised learning, named SS-ISAR-Net is proposed. It effectively mitigates grating lobes caused by sparse radar echo, allowing high-quality training to be achieved using only sparse radar echo data. The superiority of the proposed SS-ISAR-Net, compared to existing methods, is verified through experiments with both synthetic and real-world measurement data.
Near-Field Directional Modulation for RIS-Aided Movable Antenna MIMO Systems with Hardware Impairments
Authors: Maolin Li, Feng Shu, Riqing Chen, Cunhua Pan, Yongpeng Wu
Date: 2025-06-01 00:00:00
Tasks: compressed sensing

Movable antennas (MAs) are a promising technology to achieve a significant enhancement in rate for future wireless networks. The pioneering investigation on near-field directional modulation design for a reconfigurable intelligent surface (RIS)-assisted MA system is presented, with the base station equipped with a MA array. To maximize the secrecy sum rate (Max-SSR) with hardware impairments (HWIs) and imperfect channel state information (CSI), which involves a joint optimization of beamforming vectors for confidential messages and artificial noise (AN), power allocation factors, phase shift matrices, MA positions, and receive beamforming vectors. Firstly, the transmit beamforming vectors and phase shift matrices are iteratively optimized, leveraging leakage theory and phase alignment techniques. Then, two novel algorithms for discrete MA positioning are proposed, respectively, employing uniform and compressed sensing (CS)-based non-uniform grouping strategies. Subsequently, the AN is considered and designed as the additional energy required for zero-space projection, and the receive beamforming vector is derived using the minimum mean square error (MMSE) method. The proposed algorithms have low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms. Under HWIs and imperfect CSI, the proposed algorithm can achieve a 28\% enhancement in SSR performance while reducing the number of antennas by 37.5\% compared to traditional fixed-position antenna (FPA) systems.
anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding
Authors: Haitao Li, Ziyu Li, Yiheng Mao, Ziyi Liu, Zhoujian Sun, Zhengxing Huang
Date: 2025-06-01 00:00:00
Tasks: Open-Ended Question Answering, Question Answering

The advent of multimodal large language models (MLLMs) has sparked interest in their application to electrocardiogram (ECG) analysis. However, existing ECG-focused MLLMs primarily focus on report generation tasks, often limited to single 12-lead, short-duration (10s) ECG inputs, thereby underutilizing the potential of MLLMs. To this end, we aim to develop a MLLM for ECG analysis that supports a broader range of tasks and more flexible ECG inputs. However, existing ECG-QA datasets are often monotonous. To address this gap, we first constructed the anyECG dataset, which encompasses a wide variety of tasks, including report generation, abnormal waveform localization, and open-ended question answering. In addition to standard hospital ECGs, we introduced long-duration reduced-lead ECGs for home environments and multiple ECG comparison scenarios commonly encountered in clinical practice. Furthermore, we propose the anyECG-chat model, which supports dynamic-length ECG inputs and multiple ECG inputs. We trained the model using a three-stage curriculum training recipe with the anyECG dataset. A comprehensive evaluation was conducted, demonstrating that anyECG-chat is capable of supporting various practical application scenarios, including not only common report generation tasks but also abnormal waveform localization for long-duration reduced-lead ECGs in home environments and comprehensive comparative analysis of multiple ECGs.
Training Beam Design for Channel Estimation in Hybrid mmWave MIMO Systems
Authors: Xiaochun Ge, Wenqian Shen, Chengwen Xing, Lian Zhao, Jianping An
Date: 2025-06-01 00:00:00
Tasks: Compressive Sensing

Training beam design for channel estimation with infinite-resolution and low-resolution phase shifters (PSs) in hybrid analog-digital milimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems is considered in this paper. By exploiting the sparsity of mmWave channels, the optimization of the sensing matrices (corresponding to training beams) is formulated according to the compressive sensing (CS) theory. Under the condition of infinite-resolution PSs, we propose relevant algorithms to construct the sensing matrix, where the theory of convex optimization and the gradient descent in Riemannian manifold is used to design the digital and analog part, respectively. Furthermore, a block-wise alternating hybrid analog-digital algorithm is proposed to tackle the design of training beams with low-resolution PSs, where the performance degeneration caused by non-convex constant modulus and discrete phase constraints is effectively compensated to some extent thanks to the iterations among blocks. Finally, the orthogonal matching pursuit (OMP) based estimator is adopted for achieving an effective recovery of the sparse mmWave channel. Simulation results demonstrate the performance advantages of proposed algorithms compared with some existing schemes.
Uncertainty-Aware Multi-view Arrhythmia Classification from ECG
Authors: Mohd Ashhad, Sana Rahmani, Mohammed Fayiz, Ali Etemad, Javad Hashemi
Date: 2025-06-01 00:00:00

We propose a deep neural architecture that performs uncertainty-aware multi-view classification of arrhythmia from ECG. Our method learns two different views (1D and 2D) of single-lead ECG to capture different types of information. We use a fusion technique to reduce the conflict between the different views caused by noise and artifacts in ECG data, thus incorporating uncertainty to obtain stronger final predictions. Our framework contains the following three modules (1) a time-series module to learn the morphological features from ECG; (2) an image-space learning module to learn the spatiotemporal features; and (3) the uncertainty-aware fusion module to fuse the information from the two different views. Experimental results on two real-world datasets demonstrate that our framework not only improves the performance on arrhythmia classification compared to the state-of-the-art but also shows better robustness to noise and artifacts present in ECG.
Near-Field Multiuser Localization Based on Extremely Large Antenna Array with Limited RF Chains
Authors: Boyu Teng, Xiaojun Yuan, Rui Wang, Ying-Chang Liang
Date: 2025-06-01 00:00:00

Extremely large antenna array (ELAA) not only effectively enhances system communication performance but also improves the sensing capabilities of communication systems, making it one of the key enabling technologies in 6G wireless networks. This paper investigates the multiuser localization problem in an uplink Multiple Input Multiple Output (MIMO) system, where the base station (BS) is equipped with an ELAA to receive signals from multiple single-antenna users. We exploit analog beamforming to reduce the number of radio frequency (RF) chains. We first develop a comprehensive near-field ELAA channel model that accounts for the antenna radiation pattern and free space path loss. Due to the large aperture of the ELAA, the angular resolution of the array is high, which improves user localization accuracy. However, it also makes the user localization problem highly non-convex, posing significant challenges when the number of RF chains is limited. To address this issue, we use an array partitioning strategy to divide the ELAA channel into multiple subarray channels and utilize the geometric constraints between user locations and subarrays for probabilistic modeling. To fully exploit these geometric constraints, we propose the array partitioning-based location estimation with limited measurements (APLE-LM) algorithm based on the message passing principle to achieve multiuser localization. We derive the Bayesian Cramer-Rao Bound (BCRB) as the theoretical performance lower bound for our formulated near-field multiuser localization problem. Extensive simulations under various parameter configurations validate the proposed APLE-LM algorithm. The results demonstrate that APLE-LM achieves superior localization accuracy compared to baseline algorithms and approaches the BCRB at high signal-to-noise ratio (SNR).
Electromagnetically Reconfigurable Antennas for 6G: Enabling Technologies, Prototype Studies, and Research Outlook
Authors: Pinjun Zheng, Ruiqi Wang, Yuchen Zhang, Md. Jahangir Hossain, Anas Chaaban, Atif Shamim, Tareq Y. Al-Naffouri
Date: 2025-05-31 00:00:00

The transition to the sixth-generation (6G) network is anticipated to redefine wireless transceiver architectures, demanding higher adaptability and efficiency at the antenna layer. Electromagnetically reconfigurable antennas (ERAs) have emerged as a promising solution capable of dynamically reconfiguring wireless channels to meet these requirements. This article presents an overview of recent advancements in ERA technology, underscoring its transformative potential for 6G applications. Drawing from several initial studies, we demonstrate that ERAs can significantly enhance communication rates and hardware efficiency. Nevertheless, critical challenges remain in hardware design and signal processing methodologies, necessitating concerted efforts from both the antenna and communication communities. We identify these gaps and outline key research directions to fully unlock the capabilities of ERAs in next-generation wireless networks.
Real-Time Sounding in ISAC networks: Design and Implementation of a Multi-Node Testbed with Synchronized Airborne and Ground-Based Sensors
Authors: Julia Beuster, Carsten Andrich, Sebastian Giehl, Marc Miranda, Lorenz Mohr, Dieter Novotny, Tom Kaufmann, Christian Schneider, Reiner S. Thomä
Date: 2025-05-31 00:00:00
Tasks: Integrated sensing and communication, ISAC

As integrated sensing and communication (ISAC) capabilities become more prevalent in the mobile 6G radio landscape, there is a substantial opportunity to enhance situational awareness across diverse applications through multi-static radar sensing within meshed ISAC networks. To facilitate the development and testing of detection and localization algorithms across diverse scenarios, this paper introduces a synchronized distributed channel sounding testbed with airborne and ground-based multi-channel transceiver nodes with centimeter-level positioning accuracy enabled by real-time kinematic (RTK) and inertial navigation system (INS) data. Our modular experimental measurement system is designed to include stationary sensor nodes and light-weight to medium-weight mobile nodes deployable on unmanned aerial vehicles (UAVs), cars, pedestrians, and cyclists. Utilizing commercial off-the-shelf (COTS) hardware, specifically software defined radios (SDRs), the testbed encourages reproducibility in academic research laboratories. We detail the individual modules and integration steps required to achieve the specified performance. The testbed's capabilities are validated through a real-world measurement campaign, including stationary and flying sensor nodes, aimed at detecting radar targets such as vertical take-off and landing (VTOL) aircrafts, small hexacopters, cars and vulnerable road users (VRUs) in air-to-air (A2A) and air-to-ground (A2G) scenarios.
Over-the-air Multifunctional Wideband Electromagnetic Signal Processing using Dynamic Scattering Arrays
Authors: Davide Dardari
Date: 2025-05-31 00:00:00

To meet the stringent requirements of next-generation wireless networks, multiple-input multiple-output (MIMO) technology is expected to become massive and pervasive. Unfortunately, this could pose scalability issues in terms of complexity, power consumption, cost, and processing latency. Therefore, novel technologies and design approaches, such as the recently introduced holographic MIMO paradigm, must be investigated to make future networks sustainable. In this context, we investigate the concept of a dynamic scattering array (DSA) as a versatile electromagnetic (EM) structure capable of performing joint wave-based computing and radiation by moving the processing from the digital domain to the EM domain. We provide a general, wideband analytical framework for modeling the DSA, which includes a power matching network and realistic reconfigurable loads. Then we introduce specific design algorithms, and apply them to various use cases. We demonstrate that some recent EM processing structures can be seen as particular cases of our general framework. The examples presented in the numerical results corroborate the potential of DSAs to reduce complexity and the number of radiofrequency (RF) chains in holographic MIMO systems while achieving enhanced EM wave processing and radiation flexibility for tasks such as beamforming and single- and multi-user MIMO, also exhibiting superdirectivity capabilities.