pwc-archive/papers-with-abstracts huggingface dataset:
Super-temporal-resolution Photoacoustic Imaging with Dynamic Reconstruction through Implicit Neural Representation in Sparse-view
Authors: Youshen Xiao, Yiling Shi, Ruixi Sun, Hongjiang Wei, Fei Gao, Yuyao Zhang
Date: 2025-05-29 00:00:00
Tasks: Dynamic Reconstruction, Image Reconstruction

Dynamic Photoacoustic Computed Tomography (PACT) is an important imaging technique for monitoring physiological processes, capable of providing high-contrast images of optical absorption at much greater depths than traditional optical imaging methods. However, practical instrumentation and geometric constraints limit the number of acoustic sensors available around the imaging target, leading to sparsity in sensor data. Traditional photoacoustic (PA) image reconstruction methods, when directly applied to sparse PA data, produce severe artifacts. Additionally, these traditional methods do not consider the inter-frame relationships in dynamic imaging. Temporal resolution is crucial for dynamic photoacoustic imaging, which is fundamentally limited by the low repetition rate (e.g., 20 Hz) and high cost of high-power laser technology. Recently, Implicit Neural Representation (INR) has emerged as a powerful deep learning tool for solving inverse problems with sparse data, by characterizing signal properties as continuous functions of their coordinates in an unsupervised manner. In this work, we propose an INR-based method to improve dynamic photoacoustic image reconstruction from sparse-views and enhance temporal resolution, using only spatiotemporal coordinates as input. Specifically, the proposed INR represents dynamic photoacoustic images as implicit functions and encodes them into a neural network. The weights of the network are learned solely from the acquired sparse sensor data, without the need for external training datasets or prior images. Benefiting from the strong implicit continuity regularization provided by INR, as well as explicit regularization for low-rank and sparsity, our proposed method outperforms traditional reconstruction methods under two different sparsity conditions, effectively suppressing artifacts and ensuring image quality.
Automotive Radar Online Channel Imbalance Estimation via NLMS
Authors: Esmaeil Kavousi Ghafi, Oliver Lang, Matthias Wagner, Alexander Melzer, Mario Huemer
Date: 2025-06-12 00:00:00

Automotive radars are one of the essential enablers of advanced driver assistance systems (ADASs). Continuous monitoring of the functional safety and reliability of automotive radars is a crucial requirement to prevent accidents and increase road safety. One of the most critical aspects to monitor in this context is radar channel imbalances, as they are a key parameter regarding the reliability of the radar. These imbalances may originate from several parameter variations or hardware fatigues, e.g., a solder ball break (SBB), and may affect some radar processing steps, such as the angle of arrival estimation. In this work, a novel method for online estimation of automotive radar channel imbalances is proposed. The proposed method exploits a normalized least mean squares (NLMS) algorithm as a block in the processing chain of the radar to estimate the channel imbalances. The input of this block is the detected targets in the range-Doppler map of the radar on the road without any prior knowledge on the angular parameters of the targets. This property in combination with low computational complexity of the NLMS, makes the proposed method suitable for online channel imbalance estimation, in parallel to the normal operation of the radar. Furthermore, it features reduced dependency on specific targets of interest and faster update rates of the channel imbalance estimation compared to the majority of state-of-the-art methods. This improvement is achieved by allowing for multiple targets in the angular spectrum, whereas most other methods are restricted to only single targets in the angular spectrum. The performance of the proposed method is validated using various simulation scenarios and is supported by measurement results.
A Novel Signal Processing Strategy for Short-Range Laser Feedback Interferometry Sensors
Authors: Alexander Zimmer, Johannes Meyer, Enkelejda Kasneci
Date: 2025-06-12 00:00:00

The rapid evolution of wearable technologies, such as AR glasses, demands compact, energy-efficient sensors capable of high-precision measurements in dynamic environments. Traditional Frequency-Modulated Continuous Wave (FMCW) Laser Feedback Interferometry (LFI) sensors, while promising, falter in applications that feature small distances, high velocities, shallow modulation, and low-power constraints. We propose a novel sensor-processing pipeline that reliably extracts distance and velocity measurements at distances as low as 1 cm. As a core contribution, we introduce a four-ramp modulation scheme that resolves persistent ambiguities in beat frequency signs and overcomes spectral blind regions caused by hardware limitations. Based on measurements of the implemented pipeline, a noise model is defined to evaluate its performance and sensitivity to several algorithmic and working point parameters. We show that the pipeline generally achieves robust and low-noise measurements using state-of-the-art hardware.
Sum Rate Maximization for Pinching Antennas Assisted RSMA System With Multiple Waveguides
Authors: Peiyu Wang, Hong Wang, Rongfang Song
Date: 2025-06-12 00:00:00
Tasks: Benchmarking

In this letter, a pinching antennas (PAs) assisted rate splitting multiple access (RSMA) system with multiple waveguides is investigated to maximize sum rate. A two-step algorithm is proposed to determine PA activation scheme and optimize the waveguide beamforming. Specifically, a low complexity spatial correlation and distance based method is proposed for PA activation selection. After determining the PA activation status, a semi-definite programming (SDP) based successive convex approximation (SCA) is leveraged to obtain the optimal waveguide beamforming. Simulation results show that the proposed multiple waveguides based PAs assisted RSMA method achieves better performance than various benchmarking schemes.
A Neural Network-aided Low Complexity Chase Decoder for URLLC
Authors: Enrico Testi, Enrico Paolini
Date: 2025-06-12 00:00:00
Tasks: Decoder

Ultra-reliable low-latency communications (URLLC) demand decoding algorithms that simultaneously offer high reliability and low complexity under stringent latency constraints. While iterative decoding schemes for LDPC and Polar codes offer a good compromise between performance and complexity, they fall short in approaching the theoretical performance limits in the typical URLLC short block length regime. Conversely, quasi-ML decoding schemes for algebraic codes, like Chase-II decoding, exhibit a smaller gap to optimum decoding but are computationally prohibitive for practical deployment in URLLC systems. To bridge this gap, we propose an enhanced Chase-II decoding algorithm that leverages a neural network (NN) to predict promising perturbation patterns, drastically reducing the number of required decoding trials. The proposed approach combines the reliability of quasi-ML decoding with the efficiency of NN inference, making it well-suited for time-sensitive and resource-constrained applications.
Relaxation-Free Min-k-Partition for PCI Assignment in 5G Networks
Authors: Yeqing Qiu, Chengpiao Huang, Ye Xue, Zhipeng Jiang, Qingjiang Shi, Dong Zhang, Zhi-Quan Luo
Date: 2025-06-12 00:00:00
Tasks: Computational Efficiency

Physical Cell Identity (PCI) is a critical parameter in 5G networks. Efficient and accurate PCI assignment is essential for mitigating mod-3 interference, mod-30 interference, collisions, and confusions among cells, which directly affect network reliability and user experience. In this paper, we propose a novel framework for PCI assignment by decomposing the problem into Min-3-Partition, Min-10-Partition, and a graph coloring problem, leveraging the Chinese Remainder Theorem (CRT). Furthermore, we develop a relaxation-free approach to the general Min-k-Partition problem by reformulating it as a quadratic program with a norm-equality constraint and solving it using a penalized mirror descent (PMD) algorithm. The proposed method demonstrates superior computational efficiency and scalability, significantly reducing interference while eliminating collisions and confusions in large-scale 5G networks. Numerical evaluations on real-world datasets show that our approach reduces computational time by up to 20 times compared to state-of-the-art methods, making it highly practical for real-time PCI optimization in large-scale networks. These results highlight the potential of our method to improve network performance and reduce deployment costs in modern 5G systems.
Heterogeneous-IRS-Assisted MIMO Systems: Channel Estimation and Beamforming
Authors: Weibiao Zhao, Qiucen Wu, Yuanqi Tang, Yu Zhu
Date: 2025-06-12 00:00:00

Intelligent reflecting surface (IRS) has gained great attention for its ability to create favorable propagation environments. However, the power consumption of conventional IRSs cannot be ignored due to the large number of reflecting elements and control circuits. To balance performance and power consumption, we previously proposed a heterogeneous-IRS (HE-IRS), a green IRS structure integrating dynamically tunable elements (DTEs) and statically tunable elements (STEs). Compared to conventional IRSs with only DTEs, the unique DTE-STE integrated structure introduces new challenges in both channel estimation and beamforming. In this paper, we investigate the channel estimation and beamforming problems in HE-IRS-assisted multi-user multiple-input multiple-output systems. Unlike the overall cascaded channel estimated in conventional IRSs, we show that the HE-IRS channel to be estimated is decomposed into a DTE-based cascaded channel and an STE-based equivalent channel. Leveraging it along with the inherent sparsity of DTE- and STE-based channels and manifold optimization, we propose an efficient channel estimation scheme. To address the rank mismatch problem in the imperfect channel sparsity information, a robust rank selection rule is developed. For beamforming, we propose an offline algorithm to optimize the STE phase shifts for wide beam coverage, and an online algorithm to optimize the BS precoder and the DTE phase shifts using the estimated HE-IRS channel. Simulation results show that the HE-IRS requires less pilot overhead than conventional IRSs with the same number of elements. With the proposed channel estimation and beamforming schemes, the green HE-IRS achieves competitive sum rate performance with significantly reduced power consumption.
Intelligent Travel Activity Monitoring: Generalized Distributed Acoustic Sensing Approaches
Authors: Ruikang Zhong, Chia-Yen Chiang, Mona Jaber, Rupert De Wilde, Peter Hayward
Date: 2025-06-11 00:00:00
Tasks: Federated Learning, Meta-Learning, Privacy Preserving, STS

Obtaining data on active travel activities such as walking, jogging, and cycling is important for refining sustainable transportation systems (STS). Effectively monitoring these activities not only requires sensing solutions to have a joint feature of being accurate, economical, and privacy-preserving, but also enough generalizability to adapt to different climate environments and deployment conditions. In order to provide a generalized sensing solution, a deep learning (DL)-enhanced distributed acoustic sensing (DAS) system for monitoring active travel activities is proposed. By leveraging the ambient vibrations captured by DAS, this scheme infers motion patterns without relying on image-based or wearable devices, thereby addressing privacy concerns. We conduct real-world experiments in two geographically distinct locations and collect comprehensive datasets to evaluate the performance of the proposed system. To address the generalization challenges posed by heterogeneous deployment environments, we propose two solutions according to network availability: 1) an Internet-of-Things (IoT) scheme based on federated learning (FL) is proposed, and it enables geographically different DAS nodes to be trained collaboratively to improve generalizability; 2) an off-line initialization approach enabled by meta-learning is proposed to develop high-generality initialization for DL models and to enable rapid model fine-tuning with limited data samples, facilitating generalization at newly established or isolated DAS nodes. Experimental results of the walking and cycling classification problem demonstrate the performance and generalizability of the proposed DL-enhanced DAS system, paving the way for practical, large-scale DAS monitoring of active travel.
Quantifying Data Requirements for EEG Independent Component Analysis Using AMICA
Authors: Gwenevere Frank, Seyed Yahya Shirazi, Jason Palmer, Gert Cauwenberghs, Scott Makeig, Arnaud Delorme
Date: 2025-06-11 00:00:00
Tasks: EEG

Independent Component Analysis (ICA) is an important step in EEG processing for a wide-ranging set of applications. However, ICA requires well-designed studies and data collection practices to yield optimal results. Past studies have focused on quantitative evaluation of the differences in quality produced by different ICA algorithms as well as different configurations of parameters for AMICA, a multimodal ICA algorithm that is considered the benchmark against which other algorithms are measured. Here, the effect of the data quantity versus the number of channels on decomposition quality is explored. AMICA decompositions were run on a 71 channel dataset with 13 subjects while randomly subsampling data to correspond to specific ratios of the number of frames in a dataset to the channel count. Decomposition quality was evaluated for the varying quantities of data using measures of mutual information reduction (MIR) and the near dipolarity of components. We also note that an asymptotic trend can be seen in the increase of MIR and a general increasing trend in near dipolarity with increasing data, but no definitive plateau in these metrics was observed, suggesting that the benefits of collecting additional EEG data may extend beyond common heuristic thresholds and continue to enhance decomposition quality.
Deep Semantic Segmentation for Multi-Source Localization Using Angle of Arrival Measurements
Authors: Mustafa Atahan Nuhoglu, Hakan Ali Cirpan
Date: 2025-06-11 00:00:00
Tasks: Semantic Segmentation

This paper presents a solution for multi source localization using only angle of arrival measurements. The receiver platform is in motion, while the sources are assumed to be stationary. Although numerous methods exist for single source localization, many relying on pseudo-linear formulations or non convex optimization techniques, there remains a significant gap in research addressing multi source localization in dynamic environments. To bridge this gap, we propose a deep learning-based framework that leverages semantic segmentation models for multi source localization. Specifically, we employ UNet and UNetPP as backbone models, processing input images that encode the platform's positions along with the corresponding direction finding lines at each position. By analyzing the intersections of these lines, the models effectively identify and localize multiple sources. Through simulations, we evaluate both single- and multi-source localization scenarios. Our results demonstrate that while the proposed approach performs comparably to traditional methods in single source localization, it achieves accurate source localization even in challenging conditions with high noise levels and an increased number of sources.
A Reinforcement Learning Approach for RIS-aided Fair Communications
Authors: Alex Pierron, Michel Barbeau, Luca De Cicco, Jose Rubio-Hernan, Joaquin Garcia-Alfaro
Date: 2025-06-01 00:00:00
Tasks: Fairness, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reconfigurable Intelligent Surfaces (RISs) are composed of physical elements that can dynamically alter electromagnetic wave properties to enhance beamforming and leading to improvements in areas with low coverage properties. They have the potential to be combined with Reinforcement Learning (RL) techniques to achieve network performance and energy efficiency via optimization techniques. In addition to performance and energy improvements, it is also crucial to consider the concept of fair communications. RISs must ensure that User Equipment (UE) units receive their signals with adequate strength, without other UE being deprived of service due to insufficient power. In this paper, we address such a problem. We explore the fairness properties of previous work and propose a novel method that aims at obtaining an efficient and fair duplex RIS-RL system for multiple legitimate UE units. We report and discuss our experimental work and simulation results. We also release our code and datasets to foster further research in the topic.
Eigenvalue-Based Detection in MIMO Systems for Integrated Sensing and Communication
Authors: Alex Obando, Saman Atapattu, Prathapasinghe Dharmawansa, Akram Hourani, Kandeepan Sithamparanathan
Date: 2025-06-11 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper considers a MIMO Integrated Sensing and Communication (ISAC) system, where a base station simultaneously serves a MIMO communication user and a remote MIMO sensing receiver, without channel state information (CSI) at the transmitter. Existing MIMO ISAC literature often prioritizes communication rate or detection probability, typically under constant false-alarm rate (CFAR) assumptions, without jointly analyzing detection reliability and communication constraints. To address this gap, we adopt an eigenvalue-based detector for robust sensing and use a performance metric, the total detection error, that jointly captures false-alarm and missed-detection probabilities. We derive novel closed-form expressions for both probabilities under the eigenvalue detector, enabling rigorous sensing analysis. Using these expressions, we formulate and solve a joint power allocation and threshold optimization problem that minimizes total detection error while meeting a minimum communication rate requirement. Simulation results demonstrate that the proposed joint design substantially outperforms conventional CFAR-based schemes, highlighting the benefits of power- and threshold-aware optimization in MIMO ISAC systems.
Near-Field Sensing Enabled Predictive Beamforming: Fundamentals, Framework, and Opportunities
Authors: Hao Jiang, Zhaolin Wang, Yue Liu, Hyundong Shin, Arumugam Nallanathan, Yuanwei Liu
Date: 2025-06-10 00:00:00

The article proposes a novel near-field predictive beamforming framework for high-mobility wireless networks. Specifically, due to the spherical waves and non-uniform Doppler frequencies brought by the near-field region, the new ability of full-dimensional location and velocity sensing is characterized. Building on this foundation, the near-field predictive beamforming framework is proposed to proactively design beamformers for mobility users following arbitrary trajectories. Compared to the conventional far-field counterpart, the near-field predictive beamforming stands out due to: i) Prior-Knowledge-Free Prediction, and ii) Low-Complexity and Generalizable System Design. To realize these advantages, the implementation methods are discussed, followed by a case study confirming the benefits of the proposed framework. Finally, the article highlights promising research opportunities inspired by the proposed framework.
Integration of Contrastive Predictive Coding and Spiking Neural Networks
Authors: Emirhan Bilgiç, Neslihan Serap Şengör, Namık Berk Yalabık, Yavuz Selim İşler, Aykut Görkem Gelen, Rahmi Elibol
Date: 2025-06-10 00:00:00

This study examines the integration of Contrastive Predictive Coding (CPC) with Spiking Neural Networks (SNN). While CPC learns the predictive structure of data to generate meaningful representations, SNN mimics the computational processes of biological neural systems over time. In this study, the goal is to develop a predictive coding model with greater biological plausibility by processing inputs and outputs in a spike-based system. The proposed model was tested on the MNIST dataset and achieved a high classification rate in distinguishing positive sequential samples from non-sequential negative samples. The study demonstrates that CPC can be effectively combined with SNN, showing that an SNN trained for classification tasks can also function as an encoding mechanism. Project codes and detailed results can be accessed on our GitHub page: https://github.com/vnd-ogrenme/ongorusel-kodlama/tree/main/CPC_SNN
Energy efficiency of DMAs vs. conventional MIMO: a sensitivity analysis
Authors: Pablo Ramírez-Espinosa, David Morales-Jiménez, Beatriz Soret
Date: 2025-06-10 00:00:00
Tasks: Sensitivity

Motivated by the stringent and challenging need for `greener communications' in increasingly power-hungry 5G networks, this paper presents a detailed energy efficiency analysis for three different multi-antenna architectures, namely fully-digital arrays, hybrid arrays, and dynamic metasurface antennas (DMAs). By leveraging a circuital model, which captures mutual coupling, insertion losses, propagation through the waveguides in DMAs and other electromagnetic phenomena, we design a transmit Wiener filter solution for the three systems. We then use these results to analyze the energy efficiency, considering different consumption models and supplied power, and with particular focus on the impact of the physical phenomena. DMAs emerge as an efficient alternative to classical arrays across diverse tested scenarios, most notably under low transmission power, strong coupling, and scalability requirements.
Estimating Visceral Adiposity from Wrist-Worn Accelerometry
Authors: James R. Williamson, Andrew Alini, Brian A. Telfer, Adam W. Potter, Karl E. Friedl
Date: 2025-06-10 00:00:00
Tasks: Nutrition

Visceral adipose tissue (VAT) is a key marker of both metabolic health and habitual physical activity (PA). Excess VAT is highly correlated with type 2 diabetes and insulin resistance. The mechanistic basis for this pathophysiology relates to overloading the liver with fatty acids. VAT is also a highly labile fat depot, with increased turnover stimulated by catecholamines during exercise. VAT can be measured with sophisticated imaging technologies, but can also be inferred directly from PA. We tested this relationship using National Health and Nutrition Examination Survey (NHANES) data from 2011-2014, for individuals aged 20-60 years with 7 days of accelerometry data (n=2,456 men; 2,427 women) [1]. Two approaches were used for estimating VAT from activity. The first used engineered features based on movements during gait and sleep, and then ridge regression to map summary statistics of these features into a VAT estimate. The second approach used deep neural networks trained on 24 hours of continuous accelerometry. A foundation model first mapped each 10s frame into a high-dimensional feature vector. A transformer model then mapped each day's feature vector time series into a VAT estimate, which were averaged over multiple days. For both approaches, the most accurate estimates were obtained with the addition of covariate information about subject demographics and body measurements. The best performance was obtained by combining the two approaches, resulting in VAT estimates with correlations of r=0.86. These findings demonstrate a strong relationship between PA and VAT and, by extension, between PA and metabolic health risks.