pwc-archive/papers-with-abstracts huggingface dataset:
Device-Free Localization Using Multi-Link MIMO Channels in Distributed Antenna Networks
Authors: Minseok Kim, Gesi Teng, Keita Nishi, Togo Ikegami, Masamune Sato
Date: 2025-05-07 00:00:00
Tasks: Bayesian Optimization, Diversity, Image Reconstruction, Integrated sensing and communication, ISAC

This paper presented a novel device-free localization (DFL) framework based on distributed antenna networks (DANs), targeting integrated sensing and communication (ISAC) in future 6G radio access networks (RANs). In the proposed approach, radio tomographic imaging (RTI) leverages the spatial and temporal diversity of multi-link multiple-input multiple-output (MIMO) channels in DANs to improve localization accuracy. Furthermore, a prototype system was developed using software-defined radios (SDRs) operating in the sub-6 GHz band, and comprehensive evaluations were conducted under indoor conditions involving varying node densities and target types. The results demonstrate that the framework achieves sub-meter localization accuracy in most scenarios and maintains robust performance under complex multipath environments. In addition, the use of Bayesian optimization to fine-tune key parameters, such as sparsity and path thickness, led to significant improvements in image reconstruction quality and target estimation accuracy. These results demonstrate the feasibility and effectiveness of DAN-based DFL systems for accurate, robust, and scalable localization.
Cross-organ all-in-one parallel compressed sensing magnetic resonance imaging
Authors: Baoshun Shi, Zheng Liu, Xin Meng, Yan Yang
Date: 2025-05-07 00:00:00
Tasks: All, compressed sensing, Image Reconstruction, Mamba

Recent advances in deep learning-based parallel compressed sensing magnetic resonance imaging (p-CSMRI) have significantly improved reconstruction quality. However, current p-CSMRI methods often require training separate deep neural network (DNN) for each organ due to anatomical variations, creating a barrier to developing generalized medical image reconstruction systems. To address this, we propose CAPNet (cross-organ all-in-one deep unfolding p-CSMRI network), a unified framework that implements a p-CSMRI iterative algorithm via three specialized modules: auxiliary variable module, prior module, and data consistency module. Recognizing that p-CSMRI systems often employ varying sampling ratios for different organs, resulting in organ-specific artifact patterns, we introduce an artifact generation submodule, which extracts and integrates artifact features into the data consistency module to enhance the discriminative capability of the overall network. For the prior module, we design an organ structure-prompt generation submodule that leverages structural features extracted from the segment anything model (SAM) to create cross-organ prompts. These prompts are strategically incorporated into the prior module through an organ structure-aware Mamba submodule. Comprehensive evaluations on a cross-organ dataset confirm that CAPNet achieves state-of-the-art reconstruction performance across multiple anatomical structures using a single unified model. Our code will be published at https://github.com/shibaoshun/CAPNet.
mAIstro: an open-source multi-agentic system for automated end-to-end development of radiomics and deep learning models for medical imaging
Authors: Eleftherios Tzanis, Michail E. Klontzas
Date: 2025-04-30 00:00:00
Tasks: AI Agent, Classification, LLM real-life tasks, Medical Image Analysis, Medical Image Segmentation, regression

Agentic systems built on large language models (LLMs) offer promising capabilities for automating complex workflows in healthcare AI. We introduce mAIstro, an open-source, autonomous multi-agentic framework for end-to-end development and deployment of medical AI models. The system orchestrates exploratory data analysis, radiomic feature extraction, image segmentation, classification, and regression through a natural language interface, requiring no coding from the user. Built on a modular architecture, mAIstro supports both open- and closed-source LLMs, and was evaluated using a large and diverse set of prompts across 16 open-source datasets, covering a wide range of imaging modalities, anatomical regions, and data types. The agents successfully executed all tasks, producing interpretable outputs and validated models. This work presents the first agentic framework capable of unifying data analysis, AI model development, and inference across varied healthcare applications, offering a reproducible and extensible foundation for clinical and research AI integration. The code is available at: https://github.com/eltzanis/mAIstro
On the Multiangle Discrete Fractional Fourier Transform
Authors: Christian Oswald, Franz Pernkopf
Date: 2025-05-08 00:00:00

The efficiently computed multiangle centered discrete fractional Fourier transform (MA-CDFRFT) [1] has proven as a useful tool for time-frequency analysis; however, its scope is limited to the centered discrete fractional Fourier transform (CDFRFT). Meanwhile, extensive research on the standard DFRFT has lead to a better understanding of this transform as well as numerous possible choices for eigenvectors for implementation. In this letter we present a simple adaptation of the MA-CDFRFT which allows us to efficiently compute its standard counterpart, which we call the multiangle DFRFT (MA-DFRFT). Furthermore, we formalize the symmetries inherent to the MA-CDFRFT and MA-DFRFT to halve the number of FFTs needed to compute these transforms, paving the way for applications in resource constrained environments.
Rodent Breathing Waveforms in ApoE Rats: Statistical and Entropic Differentiation
Authors: Stephen E. Wormald, Nicholas J. Napoli, Gordon S. Mitchell, Alexandria B. Marciante
Date: 2025-05-08 00:00:00

Apolipoprotein E (ApoE) gene variations are involved in lipid metabolism and cholesterol transport, with the ApoE4 allele being a known risk factor associated with neurodegenerative conditions later in life. Emerging evidence suggests these genetic variations may also influence respiratory function and vitality. However, the specific impact of different ApoE genotypes on breathing patterns remains largely unexplored. This work investigates differences in breathing waveform characteristics and entropy statistics derived from plethysmography (PLETH) data between rat models possessing two distinct ApoE genotypes (referred to herein as gene59 and gene95). Findings reveal significant distributional differences in common plethysmography metrics and approximate entropy between the two genotypes, observed during both active and resting states. Additionally, the study examines the transient impact of sighs (deep breaths) on these breathing metrics, demonstrating that entropy and other measures are altered in the breaths immediately following a sigh.
Chirp-Based Aliasing Analysis of Arrays in the Spherical Wavefront Regime
Authors: Gilles Monnoyer, Laurence Defraigne, Baptiste Sambon, Jérôme Louveaux, Luc Vandendorpe
Date: 2025-05-08 00:00:00

In antenna arrays, wave propagation modeling based on Euclidean principles is typically represented by steering vectors or signals. This paper provides a new, chirp-based, interpretation of steering vectors in the Spherical Wavefront Regime (SWR), establishing a relationship between the spatial spectrum of the received (resp. transmitted) signal and the geometry of the array and the source (resp. target). Leveraging the well-known sampling theorem, we analyze aliasing effects arising from spatial sampling with a finite number of antennas and understand how these effects degrade the Ambiguity Function (AF). Our framework provides geometric insight into these degradations, offering a deeper understanding of the non-space-invariant aliasing mechanisms in the SWR. The proposed approach is formulated for general antenna arrays and then instantiated to Circular Array and to Uniform Linear Array structures operating in Near Field conditions.
Temperature-Resilient LC-RIS Phase-Shift Design for Multi-user Downlink Communications
Authors: Nairy Moghadas Gholian, Mohamadreza Delbari, Vahid Jamali, Arash Asadi
Date: 2025-05-08 00:00:00

The reflecting antenna elements in most reconfigurable intelligent surfaces (RISs) use semiconductor-based (e.g., positive-intrinsic-negative (PIN) diodes and varactors) phase shifters. Although effective, a drawback of this technology is the high power consumption and cost, which become particularly prohibitive in millimeter-wave (mmWave)/sub-Terahertz range. With the advances in Liquid Crystals (LCs) in microwave engineering, we have observed a new trend in using LC for realizing phase shifter networks of RISs. LC-RISs are expected to significantly reduce the fabrication costs and power consumption. However, the nematic LC molecules are sensitive to temperature variations. Therefore, implementing LC-RIS in geographical regions with varying temperatures requires temperature-resilient designs. The mentioned temperature variation issue becomes more significant at higher temperatures as the phase shifter range reduces in warmer conditions, whereas it expands in cooler ones. In this paper, we study the impact of temperature on the operation of LC-RISs and develop a temperature-resilient phase shift design. Specifically, we formulate a max-min signal-to-interference-plus-noise ratio optimization for a multi-user downlink mmWave network that accounts for the impact of temperature in the LC-RIS phase shifts. The simulation results demonstrate a significant improvement for the considered set of parameters when using our algorithm compared to the baseline approach, which neglects the temperature effects.
Duplex Self-Aligning Resonant Beam Communications and Power Transfer with Coupled Spatially Distributed Laser Resonator
Authors: Mingliang Xiong, Qingwen Liu, Hao Deng, Gang Wang, Gang Li, Bin He
Date: 2025-05-08 00:00:00

Sustainable energy supply and high-speed communications are two significant needs for mobile electronic devices. This paper introduces a self-aligning resonant beam system for simultaneous light information and power transfer (SLIPT), employing a novel coupled spatially distributed resonator (CSDR). The system utilizes a resonant beam for efficient power delivery and a second-harmonic beam for concurrent data transmission, inherently minimizing echo interference and enabling bidirectional communication. Through comprehensive analyses, we investigate the CSDR's stable region, beam evolution, and power characteristics in relation to working distance and device parameters. Numerical simulations validate the CSDR-SLIPT system's feasibility by identifying a stable beam waist location for achieving accurate mode-match coupling between two spatially distributed resonant cavities and demonstrating its operational range and efficient power delivery across varying distances. The research reveals the system's benefits in terms of both safety and energy transmission efficiency. We also demonstrate the trade-off among the reflectivities of the cavity mirrors in the CSDR. These findings offer valuable design insights for resonant beam systems, advancing SLIPT with significant potential for remote device connectivity.
Autoregressive Stochastic Clock Jitter Compensation in Analog-to-Digital Converters
Authors: Daniele Gerosa, Rui Hou, Vimar Björk, Ulf Gustavsson, Thomas Eriksson
Date: 2025-05-08 00:00:00
Tasks: Benchmarking

This paper deals with the mathematical modeling and compensation of stochastic discrete time clock jitter in Analog-to-Digital Converters (ADCs). Two novel, computationally efficient de-jittering sample pilots-based algorithms for baseband signals are proposed: one consisting in solving a sequence of weighted least-squares problems and another that fully leverages the correlated jitter structure in a Kalman filter-type routine. Alongside, a comprehensive and rigorous mathematical analysis of the linearization errors committed is presented, and the work is complemented with extensive synthetic simulations and performance benchmarking with the scope of gauging and stress-testing the techniques in different scenarios.
Experiment Study on Reference-Path-Aided System Calibration for mmWave Bistatic ISAC Systems
Authors: Chenhao Luo, Chongrui Wang, Aimin Tang, Fei Gao, Chaojun Xu
Date: 2025-05-08 00:00:00
Tasks: ISAC

Integrated sensing and communications (ISAC) has been regarded as a key enabling technology for next-generation wireless networks. Compared to monostatic ISAC, bistatic ISAC can eliminate the critical challenge of self-interference cancellation and is well compatible with the existing network infrastructures. However, the synchronization between the transmitter and the sensing receiver becomes a crucial problem. The extracted channel state information (CSI) for sensing under communication synchronization contains different types of system errors, such as the sampling time offset (STO), carrier frequency offset (CFO), and random phase shift, which can severely degrade sensing performance or even render sensing infeasible. To address this problem, a reference-path-aided system calibration scheme is designed for mmWave bistatic ISAC systems, where the line-of-sight (LoS) path can be blocked. By exploiting the delay-angle sparsity feature in mmWave ISAC systems, the reference path, which can be either a LoS or a non-LoS (NLoS) path, is first identified. By leveraging the fact that all the paths suffer the same system errors, the channel parameter extracted from the reference path is utilized to compensate for the system errors in all other paths. A mmWave ISAC system is developed to validate our design. Experimental results demonstrate that the proposed scheme can support precise estimation of Doppler shift and delay, maintaining time-synchronization errors within 1 nanosecond.
Over-the-Air ODE-Inspired Neural Network for Dual Task-Oriented Semantic Communications
Authors: Mengbing Liu, Jiancheng An, Chongwen Huang, Chau Yuen
Date: 2025-05-08 00:00:00
Tasks: Image Reconstruction, Semantic Communication

Analog machine-learning hardware platforms promise greater speed and energy efficiency than their digital counterparts. Specifically, over-the-air analog computation allows offloading computation to the wireless propagation through carefully constructed transmitted signals. In addition, reconfigurable intelligent surface (RIS) is emerging as a promising solution for next-generation wireless networks, offering the ability to tailor the communication environment. Leveraging the advantages of RIS, we design and implement the ordinary differential equation (ODE) neural network using over-the-air computation (AirComp) and demonstrate its effectiveness for dual tasks. We engineer the ambient wireless propagation environment through distributed RISs to create an architecture termed the over-the-air ordinary differential equation (Air-ODE) network. Unlike the conventional digital ODE-inspired neural network, the Air-ODE block utilizes the physics of wave reflection and the reconfigurable phase shifts of RISs to implement an ODE block in the analog domain, enhancing spectrum efficiency. Moreover, the advantages of Air-ODE are demonstrated in a deep learning-based semantic communication (DeepSC) system by extracting effective semantic information to reduce the data transmission load, while achieving the dual functions of image reconstruction and semantic tagging simultaneously at the receiver. Simulation results show that the analog Air-ODE network can achieve similar performance to the digital ODE-inspired network. Specifically, for the image reconstruction and semantic tagging task, compared with the analog network without the Air-ODE block, the Air-ODE block can achieve around 2 times gain in both reconstruction quality and tagging accuracy.
Image Steganography For Securing Intellicise Wireless Networks: "Invisible Encryption" Against Eavesdroppers
Authors: Bizhu Wang, Song Gao, Rui Meng, Haixiao Gao, Xiaodong Xu, Mengying Sun, Chen Dong, Ping Zhang, Dusit Niyato
Date: 2025-05-07 00:00:00
Tasks: Image Steganography, Semantic Communication

As one of the most promising technologies for intellicise (intelligent and consice) wireless networks, Semantic Communication (SemCom) significantly improves communication efficiency by extracting, transmitting, and recovering semantic information, while reducing transmission delay. However, an integration of communication and artificial intelligence (AI) also exposes SemCom to security and privacy threats posed by intelligent eavesdroppers. To address this challenge, image steganography in SemCom embeds secret semantic features within cover semantic features, allowing intelligent eavesdroppers to decode only the cover image. This technique offers a form of "invisible encryption" for SemCom. Motivated by these advancements, this paper conducts a comprehensive exploration of integrating image steganography into SemCom. Firstly, we review existing encryption techniques in SemCom and assess the potential of image steganography in enhancing its security. Secondly, we delve into various image steganographic paradigms designed to secure SemCom, encompassing three categories of joint source-channel coding (JSCC) models tailored for image steganography SemCom, along with multiple training strategies. Thirdly, we present a case study to illustrate the effectiveness of coverless steganography SemCom. Finally, we propose future research directions for image steganography SemCom.
Meta-Learning Driven Lightweight Phase Shift Compression for IRS-Assisted Wireless Systems
Authors: Xianhua Yu, Dong Li, Bowen Gu, Xiaoye Jing, Wen Wu, Tuo Wu, Kan Yu
Date: 2025-05-07 00:00:00
Tasks: Decoder, Meta-Learning

The phase shift information (PSI) overhead poses a critical challenge to enabling real-time intelligent reflecting surface (IRS)-assisted wireless systems, particularly under dynamic and resource-constrained conditions. In this paper, we propose a lightweight PSI compression framework, termed meta-learning-driven compression and reconstruction network (MCRNet). By leveraging a few-shot adaptation strategy via model-agnostic meta-learning (MAML), MCRNet enables rapid generalization across diverse IRS configurations with minimal retraining overhead. Furthermore, a novel depthwise convolutional gating (DWCG) module is incorporated into the decoder to achieve adaptive local feature modulation with low computational cost, significantly improving decoding efficiency. Extensive simulations demonstrate that MCRNet achieves competitive normalized mean square error performance compared to state-of-the-art baselines across various compression ratios, while substantially reducing model size and inference latency. These results validate the effectiveness of the proposed asymmetric architecture and highlight the practical scalability and real-time applicability of MCRNet for dynamic IRS-assisted wireless deployments.
Phase Shift Information Compression in IRS-aided Wireless Systems: Challenges and Opportunities
Authors: Xianhua Yu, Dong Li
Date: 2025-05-07 00:00:00
Tasks: Meta-Learning

Intelligent reflecting surfaces (IRS) have emerged as a promising technology for future 6G wireless networks, offering programmable control of the wireless environment by adjusting the phase shifts of reflecting elements. However, IRS performance relies on accurately configuring the phase shifts of reflecting elements, which introduces substantial phase shift information (PSI) delivery overhead, especially in large-scale or rapidly changing environments. This paper first introduces the architecture of IRS-assisted systems and highlights real-world use cases where PSI delivery becomes a critical bottleneck. It then reviews current PSI compression approaches, outlining their limitations in adaptability and scalability. To address these gaps, we propose a prompt-guided PSI compression framework that leverages task-aware prompts and meta-learning to achieve efficient and real-time PSI delivery under diverse conditions. Simulation results show improved reconstruction accuracy and robustness compared to the baseline method. Finally, we discuss open challenges and outline promising directions for future research.
SwinLSTM Autoencoder for Temporal-Spatial-Frequency Domain CSI Compression in Massive MIMO Systems
Authors: Aakash Saini, Yunchou Xing, Jee Hyun Kim, Amir Ahmadian Tehrani, Wolfgang Gerstacker
Date: 2025-05-07 00:00:00

This study presents a parameter-light, low-complexity artificial intelligence/machine learning (AI/ML) model that enhances channel state information (CSI) feedback in wireless systems by jointly exploiting temporal, spatial, and frequency (TSF) domain correlations. While traditional frameworks use autoencoders for CSI compression at the user equipment (UE) and reconstruction at the network (NW) side in spatial-frequency (SF), massive multiple-input multiple-output (mMIMO) systems in low mobility scenarios exhibit strong temporal correlation alongside frequency and spatial correlations. An autoencoder architecture alone is insufficient to exploit the TSF domain correlation in CSI; a recurrent element is also required. To address the vanishing gradients problem, researchers in recent works have proposed state-of-the-art TSF domain CSI compression architectures that combine recurrent networks for temporal correlation exploitation with deep pre-trained autoencoder that handle SF domain CSI compression. However, this approach increases the number of parameters and computational complexity. To jointly utilize correlations across the TSF domain, we propose a novel, parameter-light, low-complexity AI/ML-based recurrent autoencoder architecture to compress CSI at the UE side and reconstruct it on the NW side while minimizing CSI feedback overhead.
Near-Field MIMO Channel Acquisition: Geometry-Aided Feedback and Transmission Design
Authors: Shima Eslami, Bikshapathi Gouda, Antti Tölli
Date: 2025-05-07 00:00:00

Near-field (NF) line-of-sight (LoS) MIMO systems enable efficient channel state information (CSI) acquisition and precoding by exploiting known antenna geometries at both the base station (BS) and user equipment (UE). This paper introduces a compact parameterization of the NF LoS MIMO channel using two angles of departure (AoDs) and a BS-UE relative rotation angle. The inclusion of the second AoD removes the need for fine-grained distance grids imposed by conventional NF channel parametrization. To address the user-specific uplink pilot overhead in multiuser NF CSI acquisition, we propose a scheme that uses a fixed, UE-independent set of downlink pilots transmitted from a carefully selected subset of BS antennas. In dominant LoS conditions, as few as four pilots suffice, with Cram\'er-Rao bound (CRB) analysis confirming that increased antenna spacing improves estimation accuracy. Each UE estimates and quantizes its angular parameters and feeds them back to the BS for geometry-based CSI reconstruction, eliminating the need for full channel feedback. To enhance robustness against noise, quantization errors, and non-line-of-sight (NLoS) components, we introduce a two-stage precoding method. The initial precoding is computed from estimated LoS CSI and refined through bidirectional over-the-air (OTA) training. Furthermore, a two-step stream allocation strategy reduces pilot and computational overhead. Simulations demonstrate that the proposed approach achieves high data rates with significantly fewer OTA iterations, approaching the performance of perfect CSI.