pwc-archive/papers-with-abstracts huggingface dataset:
Physics-Informed Neural Network for Parameter Identification: a Buck Converter Case Study
Authors: Shuyu Ou, Subham Sahoo, Ariya Sangwongwanich, Frede Blaabjerg, Mahyar Hassanifar, Martin Votava, Marius Langwasser, Marco Liserre
Date: 2025-04-29 00:00:00

System-level condition monitoring methods estimate the electrical parameters of multiple components in a converter to assess their health status. The estimation accuracy and variation can differ significantly across parameters. For instance, inductance estimations are generally more accurate and stable than inductor resistance in a buck converter. However, these performance differences remain to be analyzed with a more systematic approach otherwise the condition monitoring results can be unreliable. Therefore, this paper analyzes the training loss landscape against multiple parameters of a buck converter to provide a systematic explanation of different performances. If the training loss is high and smooth, the estimated circuit parameter typically is accurate and has low variation. Furthermore, a novel physics-informed neural network (PINN) is proposed, offering faster convergence and lower computation requirements compared to an existing PINN method. The proposed method is validated through simulations, where the loss landscape identifies the unreliable parameter estimations, and the PINN can estimate the remaining parameters.
Histogram-Probabilistic Multi-Hypothesis Tracking with Integrated Target Existence
Authors: Lukas Herrmann, Ángel F. García-Fernández, Edmund F. Brekke
Date: 2025-04-29 00:00:00
Tasks: Management

The histogram-probabilistic multi-hypothesis tracker (H-PMHT) is a parametric approach to solving the multi-target track-before-detect (TBD) problem, using expectation maximisation (EM). A key limitation of this method is the assumption of a known and constant number of targets. In this paper, we propose the integrated existence Poisson histogram probabilistic multi-hypothesis tracker (IE-PHPMHT), for TBD of multiple targets. It extends the H-PMHT framework by adding a probability of existence to each potential target. For the derivation, we utilise a Poisson point process (PPP) measurement model and Bernoulli targets, allowing for a multi-Bernoulli birth process and an unknown, time-varying number of targets. Hence, integrated track management is achieved through the discrimination of track quality assessments based on existence probabilities. The algorithm is evaluated in a simulation study of two scenarios and is compared with several other algorithms, demonstrating its performance.
Distributed U6G ELAA Communication Systems: Channel Measurement and Small-Scale Fading Characterization
Authors: Jiachen Tian, Zhengtao Jin, Xiayang Chen, Yu Han, Shi Jin, Wenjin Wang, Chao-Kai Wen
Date: 2025-04-29 00:00:00

The distributed upper 6 GHz (U6G) extra-large scale antenna array (ELAA) is a key enabler for future wireless communication systems, offering higher throughput and wider coverage, similar to existing ELAA systems, while effectively mitigating unaffordable complexity and hardware overhead. Uncertain channel characteristics, however, present significant bottleneck problems that hinder the hardware structure and algorithm design of the distributed U6G ELAA system. In response, we construct a U6G channel sounder and carry out extensive measurement campaigns across various typical scenarios. Initially, U6G channel characteristics, particularly small-scale fading characteristics, are unveiled and compared across different scenarios. Subsequently, the U6G ELAA channel characteristics are analyzed using a virtual array comprising 64 elements. Furthermore, inspired by the potential for distributed processing, we investigate U6G ELAA channel characteristics from the perspectives of subarrays and sub-bands, including subarray-wise nonstationarities, consistencies, far-field approximations, and sub-band characteristics. Through a combination of analysis and measurement validation, several insights and benefits, particularly suitable for distributed processing in U6G ELAA systems, are revealed, which provides practical validation for the deployment of U6G ELAA systems.
Task-Oriented Semantic Communication with Importance-Aware Rate Control
Authors: Zhiye Sun, Shuai Ma, Shiyin Li
Date: 2025-04-29 00:00:00
Tasks: Semantic Communication

Semantic communication is recognized for its high compression efficiency and robust resistance to noise. However, utilizing a fixed transmission rate in environments with dynamic signal-to-noise ratios (SNR) often results in inefficient use of communication resources. To address this challenge, this letter proposes an importance-aware rate control semantic communication (IRCSC) scheme, which dynamically adjusts transmission rates in response to both channel conditions and semantic importance. The scheme employs a contribution-based importance analyzer to rank semantic importance. Additionaly, a novel metric, the semantic transmission integrity index (STII), is proposed to quantify the amount of correctly transmitted information and to correlate it with inference performance. Simulations indicate that, with low computational complexity, IRCSC guarantees a controllable trade-off between performance and rate, delivering higher compression efficiency and improved task performance in high-SNR scenarios.
A High-Resolution Transmission Line Model with De-embedding Structure for Ultralow Contact Resistivity Extraction
Authors: Xuanyu Jia, Hongxu Liao, Ming Li
Date: 2025-04-29 00:00:00

In this article, we present a contact resistivity extraction method calibrated using a de-embedding structure, called High-Resolution Transmission Line Model (HR-TLM). HR-TLM has the similar infrastructure with Refined TLM (RTLM) or Refined-Ladder TLM(R-LTLM), but is optimized for calibration methods. Its advantage lies in maintaining low \r{ho}_c extraction accuracy while significantly reducing the impact of structural process errors. According to the error analysis model, we verify that the extraction accuracy of HR-TLM based on R-LTLM can reach 10-9 {\Omega}cm2 at micron scale lithography precision.
Generative Diffusion Models for Resource Allocation in Wireless Networks
Authors: Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti, Alejandro Ribeiro
Date: 2025-04-28 00:00:00
Tasks: Graph Neural Network

This paper proposes a supervised training algorithm for learning stochastic resource allocation policies with generative diffusion models (GDMs). We formulate the allocation problem as the maximization of an ergodic utility function subject to ergodic Quality of Service (QoS) constraints. Given samples from a stochastic expert policy that yields a near-optimal solution to the problem, we train a GDM policy to imitate the expert and generate new samples from the optimal distribution. We achieve near-optimal performance through sequential execution of the generated samples. To enable generalization to a family of network configurations, we parameterize the backward diffusion process with a graph neural network (GNN) architecture. We present numerical results in a case study of power control in multi-user interference networks.
Ku-Band AlScn-On-Diamond SAW Resonators with Phase Velocity above 8600 m/s
Authors: Tzu-Hsuan Hsu, Kapil Saha, Jack Kramer, Omar Barrera, Pietro Simoni, Matteo Rinaldi, Ruochen Lu
Date: 2025-04-28 00:00:00

In this work, an Aluminum Scandium Nitride (AlScN) on Diamond Sezawa-mode surface acoustic wave (SAW) platform for RF filtering at Ku-band (12-18 GHz) is demonstrated. Thanks to the high acoustic velocity and low-loss diamond substrate, the prototype resonator at 12.9 GHz achieves a high phase velocity ($v_p$) of 8671 m/s, a maximum Bode-$Q$ of 408, and coupling coefficient ($k_{\mathrm{eff}}^2$) of 2.1%, outperforming high-velocity substrates such as SiC and sapphire by more than 20% in velocity. Resonators spanning 8-18 GHz are presented. The platform's high power handling above 12.5 dBm is also experimentally validated.
Reinforcement Learning-Based Heterogeneous Multi-Task Optimization in Semantic Broadcast Communications
Authors: Zhilin Lu, Rongpeng Li, Zhifeng Zhao, Honggang Zhang
Date: 2025-04-28 00:00:00
Tasks: image-classification, Image Classification, Reinforcement Learning (RL)

Semantic broadcast communications (Semantic BC) for image transmission have achieved significant performance gains for single-task scenarios. Nevertheless, extending these methods to multi-task scenarios remains challenging, as different tasks typically require distinct objective functions, leading to potential conflicts within the shared encoder. In this paper, we propose a tri-level reinforcement learning (RL)-based multi-task Semantic BC framework, termed SemanticBC-TriRL, which effectively resolves such conflicts and enables the simultaneous support of multiple downstream tasks at the receiver side, including image classification and content reconstruction tasks. Specifically, the proposed framework employs a bottom-up tri-level alternating learning strategy, formulated as a constrained multi-objective optimization problem. At the first level, task-specific decoders are locally optimized using supervised learning. At the second level, the shared encoder is updated via proximal policy optimization (PPO), guided by task-oriented rewards. At the third level, a multi-gradient aggregation-based task weighting module adaptively adjusts task priorities and steers the encoder optimization. Through this hierarchical learning process, the encoder and decoders are alternately trained, and the three levels are cohesively integrated via constrained learning objective. Besides, the convergence of SemanticBC-TriRL is also theoretically established. Extensive simulation results demonstrate the superior performance of the proposed framework across diverse channel conditions, particularly in low SNR regimes, and confirm its scalability with increasing numbers of receivers.
Low-Complexity Channel Estimation Protocol for Non-Diagonal RIS-Assisted Communications
Authors: Mostafa Samy, Hayder Al-Hraishawi, Abuzar B. M. Adam, Madyan Alsenwi, Symeon Chatzinotas, Björn Otteresten
Date: 2025-04-28 00:00:00

Non-diagonal reconfigurable intelligent surfaces (RISs) offer enhanced wireless signal manipulation over conventional RIS by enabling the incident signal on any of its $M$ elements to be reflected from another element via an $M \times M$ switch array. To fully exploit this flexible configuration, the acquisition of individual channel state information (CSI) is essential. However, due to the passive nature of the RIS, cascaded channel estimation is performed, as the RIS itself lacks signal processing capabilities. This entails estimating the CSI for the $M \times M$ switch array cascaded channels, resulting in estimating $M^2$ coefficients, to identify the optimal configuration of the non-diagonal RIS that maximizes the channel gain. In this paper, we propose a low-complexity channel estimation protocol that substantially reduces the estimation overhead from the exhaustive $M^2$ coefficients to only $3M$ coefficients for both single-input single-output (SISO) and multiple-input single-output (MISO) systems. Specifically, a three-stage pilot-based protocol is proposed to estimate scaled versions of the user-RIS and RIS-base-station (BS) channels in the first two stages using the least square (LS) estimator and the commonly used ON/OFF protocol for conventional RIS. These scaled estimates enable the optimization of the switch array. In the third stage, the cascaded user-RIS-BS channels are estimated to enable efficient beamforming optimization. Complexity analysis shows that our proposed protocol significantly reduces the BS estimation complexity from $\mathcal{O}(NM^2)$ to $\mathcal{O}(NM)$, where $N$ is the number of BS antennas. This complexity is similar to the ON/OFF-based LS estimation for conventional diagonal RIS.
Sum-Rate Optimisation of a Multi-User STAR-RIS-Aided System with Low Complexity
Authors: Sadaf Syed, Wolfgang Utschick, Michael Joham
Date: 2025-04-28 00:00:00

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. However, the conventional RIS can only reflect the incident signal. Hence, it provides a limited coverage, as compared to a simultaneously transmitting and reflecting RIS (STAR-RIS). Prior works on the STAR-RIS address the power minimisation or the sum-rate maximisation problem by reformulating the objective problem as a convex optimisation problem and then employing numerical tools like CVX to obtain the solution, which introduces significant computational complexity leading to a huge runtime, making the algorithms impractical for real-world implementation. In this paper, we propose a low complexity solution for the optimisation of a multi-user STAR-RIS system, where the non-convex optimisation problem is decomposed into multiple convex sub-problems with closed-form optimal solutions. The simulation results illustrate that our proposed algorithm achieves similar performance to CVX-based solutions in the literature while being computationally efficient.
A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical Imaging
Authors: YoungJoon Lee, Jinu Gong, Joonhyuk Kang
Date: 2025-04-28 00:00:00
Tasks: Federated Learning, Kolmogorov-Arnold Networks, Privacy Preserving

Federated Learning (FL) enables model training across decentralized devices without sharing raw data, thereby preserving privacy in sensitive domains like healthcare. In this paper, we evaluate Kolmogorov-Arnold Networks (KAN) architectures against traditional MLP across six state-of-the-art FL algorithms on a blood cell classification dataset. Notably, our experiments demonstrate that KAN can effectively replace MLP in federated environments, achieving superior performance with simpler architectures. Furthermore, we analyze the impact of key hyperparameters-grid size and network architecture-on KAN performance under varying degrees of Non-IID data distribution. Additionally, our ablation studies reveal that optimizing KAN width while maintaining minimal depth yields the best performance in federated settings. As a result, these findings establish KAN as a promising alternative for privacy-preserving medical imaging applications in distributed healthcare. To the best of our knowledge, this is the first comprehensive benchmark of KAN in FL settings for medical imaging task.
Towards Robust Multimodal Physiological Foundation Models: Handling Arbitrary Missing Modalities
Authors: Wei-Bang Jiang, Xi Fu, Yi Ding, Cuntai Guan
Date: 2025-04-28 00:00:00
Tasks: EEG, Emotion Recognition

Multimodal physiological signals, such as EEG, ECG, EOG, and EMG, are crucial for healthcare and brain-computer interfaces. While existing methods rely on specialized architectures and dataset-specific fusion strategies, they struggle to learn universal representations that generalize across datasets and handle missing modalities at inference time. To address these issues, we propose PhysioOmni, a foundation model for multimodal physiological signal analysis that models both homogeneous and heterogeneous features to decouple multimodal signals and extract generic representations while maintaining compatibility with arbitrary missing modalities. PhysioOmni trains a decoupled multimodal tokenizer, enabling masked signal pre-training via modality-invariant and modality-specific objectives. To ensure adaptability to diverse and incomplete modality combinations, the pre-trained encoders undergo resilient fine-tuning with prototype alignment on downstream datasets. Extensive experiments on four downstream tasks, emotion recognition, sleep stage classification, motor prediction, and mental workload detection, demonstrate that PhysioOmni achieves state-of-the-art performance while maintaining strong robustness to missing modalities. Our code and model weights will be released.
Semantic Packet Aggregation for Token Communication via Genetic Beam Search
Authors: Seunghun Lee, Jihong Park, Jinho Choi, HyunCheol Park
Date: 2025-04-28 00:00:00

Token communication (TC) is poised to play a pivotal role in emerging language-driven applications such as AI-generated content (AIGC) and wireless language models (LLMs). However, token loss caused by channel noise can severely degrade task performance. To address this, in this article, we focus on the problem of semantics-aware packetization and develop a novel algorithm, termed semantic packet aggregation with genetic beam search (SemPA-GBeam), which aims to maximize the average token similarity (ATS) over erasure channels. Inspired from the genetic algorithm (GA) and the beam search algorithm, SemPA-GBeam iteratively optimizes token grouping for packetization within a fixed number of groups (i.e., fixed beam width in beam search) while randomly swapping a fraction of tokens (i.e., mutation in GA). Experiments on the MS-COCO dataset demonstrate that SemPA-GBeam achieves ATS and LPIPS scores comparable to exhaustive search while reducing complexity by more than 20x.
Deployment Optimization for XL-IRS Assisted Multi-User Communications
Authors: Chao Zhou, Changsheng You, Tianyu Liu, Bin Lyu
Date: 2025-04-28 00:00:00

In this paper, we study the deployment optimization for an extremely large-scale intelligent reflecting surface (XL-IRS) assisted multi-user communication system, within which the channels between the XL-IRS and the BS (or user) are modeled by the near-field spherical wavefronts. To draw some valuable insights, we first consider the single-user case, where an alternating optimization (AO) based algorithm is devised to maximize the received signal-to-noise ratio (SNR) at the user. To address the high computational complexity issue incurred by the AO based algorithm, three approximate received SNR expressions are obtained to yield useful insights, corresponding to the upper bound, approximate expression, and closed-form. It is demonstrated that the XL-IRS ought to be positioned near the user (rather than the BS) to obtain a higher beamforming gain. Then, for the multi-user scenario, an efficient algorithm is proposed to obtain a high-quality XL-IRS placement solution by using the AO and successive convex approximation (SCA) techniques. Furthermore, the effective degree of freedom (DoF) of the BS-IRS channel is provided, which indicates that the additional effective DoF can be leveraged to improve multi-user spatial multiplexing. Last, numerical results confirm the existence of a trade-off between near-field beam-focusing gain and multiplexing gain.
A Model-based DNN for Learning HMIMO Beamforming
Authors: ShiYong Chen, Shengqian Han
Date: 2025-04-28 00:00:00

Holographic MIMO (HMIMO) is a promising technique for large-scale MIMO systems to enhance spectral efficiency while maintaining low hardware cost and power consumption. Existing alternating optimization algorithms can effectively optimize the hybrid beamforming of HMIMO to improve the system performance, while their high computational complexity hinders real-time application. In this paper, we propose a model-based deep neural network (MB-DNN), which leverages permutation equivalent properties and the optimal beamforming structure to jointly optimize the holographic and digital beamforming. Simulation results demonstrate that the proposed MB-DNN outperforms benchmark schemes and requires much less inference time than existing alternating optimization algorithms.
Novel Selection Schemes for Multi-RIS-Assisted Fluid Antenna Systems
Authors: Mahmoud Aldababsa, Taissir Y. Elganimi, Mahmoud A. Albreem, Saeed Abdallah
Date: 2025-04-28 00:00:00

This paper investigates the performance of a multi-reconfigurable intelligent surface (RIS)-assisted fluid antenna system (FAS). In this system, a single-antenna transmitter communicates with a receiver equipped with a planar FAS through multiple RISs in the absence of a direct link. To enhance the system performance, we propose two novel selection schemes: \textit{Max-Max} and \textit{Max-Sum}. In particular, the \textit{Max-Max} scheme selects the best combination of a single RIS and a single fluid antenna (FA) port that offers the maximum signal-to-noise ratio (SNR) at the receiver. On the other hand, the \textit{Max-Sum} scheme selects one RIS while activating all FA ports providing the highest overall SNR. We conduct a detailed performance analysis of the proposed system under Nakagami-$m$ fading channels. First, we derive the cumulative distribution function (CDF) of the SNR for both selection schemes. The derived CDF is then used to obtain approximate theoretical expressions for the outage probability (OP) and the delay outage rate (DOR). Next, a high-SNR asymptotic analysis is carried out to provide further insights into the system performance in terms of diversity and coding gains. Finally, the analytical results are validated through extensive Monte Carlo simulations, demonstrating their accuracy and providing a comprehensive understanding of the system's performance.
UNet with Axial Transformer : A Neural Weather Model for Precipitation Nowcasting
Authors: Maitreya Sonawane, Sumit Mamtani
Date: 2025-04-28 00:00:00
Tasks: SSIM, Time Series

Making accurate weather predictions can be particularly challenging for localized storms or events that evolve on hourly timescales, such as thunderstorms. Hence, our goal for the project was to model Weather Nowcasting for making highly localized and accurate predictions that apply to the immediate future replacing the current numerical weather models and data assimilation systems with Deep Learning approaches. A significant advantage of machine learning is that inference is computationally cheap given an already-trained model, allowing forecasts that are nearly instantaneous and in the native high resolution of the input data. In this work we developed a novel method that employs Transformer-based machine learning models to forecast precipitation. This approach works by leveraging axial attention mechanisms to learn complex patterns and dynamics from time series frames. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings data. This paper represents an initial research on the dataset used in the domain of next frame prediciton, and hence, we demonstrate state-of-the-art results in terms of metrices (PSNR = 47.67, SSIM = 0.9943) used for the given dataset using UNet with Axial Transformer.
Target Detection for ISAC with TDD Transmission
Authors: Marcus Henninger, Lucas Giroto de Oliveira, Stephan Saur, Artjom Grudnitsky, Thorsten Wild, Silvio Mandelli
Date: 2025-04-27 00:00:00
Tasks: Integrated sensing and communication, ISAC, valid

Integrated sensing and communication (ISAC) poses various challenges that arise from the communication-centric design of cellular networks. One of them is target detection with time division duplex (TDD) transmission used in current 5G and future 6G deployments, where the periodic on-off behavior of the transmitter creates impulsive sidelobes in the radar point spread function (PSF). These can be mistaken for actual targets by conventional peak detection techniques, leading to false alarms. In this work, we first analytically describe the range-Doppler PSF due to TDD windowing. We then propose a computationally efficient method that leverages the PSF to distinguish impulsive sidelobes from valid target peaks. Simulation results and outdoor drone measurements with an ISAC proof of concept demonstrate the capability of our algorithm, showing that it can achieve reliable target detection while limiting false alarms.