pwc-archive/papers-with-abstracts huggingface dataset:
ECLARE: Efficient cross-planar learning for anisotropic resolution enhancement
Authors: Samuel W. Remedios, Shuwen Wei, Shuo Han, Jinwei Zhang, Aaron Carass, Kurt G. Schilling, Dzung L. Pham, Jerry L. Prince, Blake E. Dewey
Date: 2025-03-14 00:00:00
Tasks: Super-Resolution

In clinical imaging, magnetic resonance (MR) image volumes are often acquired as stacks of 2D slices with decreased scan times, improved signal-to-noise ratio, and image contrasts unique to 2D MR pulse sequences. While this is sufficient for clinical evaluation, automated algorithms designed for 3D analysis perform poorly on multi-slice 2D MR volumes, especially those with thick slices and gaps between slices. Super-resolution (SR) methods aim to address this problem, but previous methods do not address all of the following: slice profile shape estimation, slice gap, domain shift, and non-integer or arbitrary upsampling factors. In this paper, we propose ECLARE (Efficient Cross-planar Learning for Anisotropic Resolution Enhancement), a self-SR method that addresses each of these factors. ECLARE uses a slice profile estimated from the multi-slice 2D MR volume, trains a network to learn the mapping from low-resolution to high-resolution in-plane patches from the same volume, and performs SR with anti-aliasing. We compared ECLARE to cubic B-spline interpolation, SMORE, and other contemporary SR methods. We used realistic and representative simulations so that quantitative performance against ground truth can be computed, and ECLARE outperformed all other methods in both signal recovery and downstream tasks. Importantly, as ECLARE does not use external training data it cannot suffer from domain shift between training and testing. Our code is open-source and available at https://www.github.com/sremedios/eclare.
Generalization of Video-Based Heart Rate Estimation Methods To Low Illumination and Elevated Heart Rates
Authors: Bhargav Acharya, William Saakyan, Barbara Hammer, Hanna Drimalla
Date: 2025-03-11 00:00:00
Tasks: Deep Learning, Heart rate estimation

Heart rate is a physiological signal that provides information about an individual's health and affective state. Remote photoplethysmography (rPPG) allows the estimation of this signal from video recordings of a person's face. Classical rPPG methods make use of signal processing techniques, while recent rPPG methods utilize deep learning networks. Methods are typically evaluated on datasets collected in well-lit environments with participants at resting heart rates. However, little investigation has been done on how well these methods adapt to variations in illumination and heart rate. In this work, we systematically evaluate representative state-of-the-art methods for remote heart rate estimation. Specifically, we evaluate four classical methods and four deep learning-based rPPG estimation methods in terms of their generalization ability to changing scenarios, including low lighting conditions and elevated heart rates. For a thorough evaluation of existing approaches, we collected a novel dataset called CHILL, which systematically varies heart rate and lighting conditions. The dataset consists of recordings from 45 participants in four different scenarios. The video data was collected under two different lighting conditions (high and low) and normal and elevated heart rates. In addition, we selected two public datasets to conduct within- and cross-dataset evaluations of the rPPG methods. Our experimental results indicate that classical methods are not significantly impacted by low-light conditions. Meanwhile, some deep learning methods were found to be more robust to changes in lighting conditions but encountered challenges in estimating high heart rates. The cross-dataset evaluation revealed that the selected deep learning methods underperformed when influencing factors such as elevated heart rates and low lighting conditions were not present in the training set.
Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations
Authors: Kyriakos Stylianopoulos, Panagiotis Gavriilidis, Gabriele Gradoni, George C. Alexandropoulos
Date: 2025-03-18 00:00:00
Tasks: Graph Attention, Image Reconstruction

Radio-Frequency (RF) imaging concerns the digital recreation of the surfaces of scene objects based on the scattered field at distributed receivers. To solve this difficult inverse scattering problems, data-driven methods are often employed that extract patterns from similar training examples, while offering minimal latency. In this paper, we first provide an approximate yet fast electromagnetic model, which is based on the electric field integral equations, for data generation, and subsequently propose a Deep Neural Network (DNN) architecture to learn the corresponding inverse model. A graph-attention backbone allows for the system geometry to be passed to the DNN, where residual convolutional layers extract features about the objects, while a UNet head performs the final image reconstruction. Our quantitative and qualitative evaluations on two synthetic data sets of different characteristics showcase the performance gains of thee proposed advanced architecture and its relative resilience to signal noise levels and various reception configurations.
Near-Field Beam Prediction Using Far-Field Codebooks in Ultra-Massive MIMO Systems
Authors: Ahmed Hussain, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil
Date: 2025-03-18 00:00:00
Tasks: Beam Prediction

Ultra-massive multiple-input multiple-output (UM-MIMO) technology is a key enabler for 6G networks, offering exceptional high data rates in millimeter-wave (mmWave) and Terahertz (THz) frequency bands. The deployment of large antenna arrays at high frequencies transitions wireless communication into the radiative near-field, where precise beam alignment becomes essential for accurate channel estimation. Unlike far-field systems, which rely on angular domain only, near-field necessitates beam search across both angle and distance dimensions, leading to substantially higher training overhead. To address this challenge, we propose a discrete Fourier transform (DFT) based beam alignment to mitigate the training overhead. We highlight that the reduced path loss at shorter distances can compensate for the beamforming losses typically associated with using far-field codebooks in near-field scenarios. Additionally, far-field beamforming in the near-field exhibits angular spread, with its width determined by the user's range and angle. Leveraging this relationship, we develop a correlation interferometry (CI) algorithm, termed CI-DFT, to efficiently estimate user angle and range parameters. Simulation results demonstrate that the proposed scheme achieves performance close to exhaustive search in terms of achievable rate while significantly reducing the training overhead by 87.5%.
Equiripple MIMO Beampattern Synthesis using Chebyshev Approximation
Authors: David A. Hague, David G. Felton
Date: 2025-03-18 00:00:00

This letter presents a method for synthesizing equiripple MIMO transmit beampatterns using Chebyshev approximation. The MIMO beampattern is represented as a non-negative real-valued trigonometric polynomial where the $\ell^{\text{th}}$ order polynomial coefficient is the sum of the $\ell^{\text{th}}$ order diagonal of the waveform correlation matrix. The optimal coefficients for a given equiripple beampattern design is then posed as a Chebyshev approximation problem which is efficiently solved using the Parks-McClellan algorithm from optimal Finite Impulse Response (FIR) filter design theory. The unique advantage of this synthesis method is that it provides a closed form method to generating MIMO correlation matrices that realize the desired equiripple beampattern. This correspondingly facilitates the design of waveform sets that closely approximate those correlation matrices. This method is demonstrated via two illustrative design examples; the first using traditional partial signal correlation methods and the second using transmit beamspace processing. Both examples realize equiripple beampatterns using constant envelope and spectrally compact waveform sets.
Deep Reinforcement Learning-based Video-Haptic Radio Resource Slicing in Tactile Internet
Authors: Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang
Date: 2025-03-18 00:00:00
Tasks: Deep Reinforcement Learning

Enabling video-haptic radio resource slicing in the Tactile Internet requires a sophisticated strategy to meet the distinct requirements of video and haptic data, ensure their synchronized transmission, and address the stringent latency demands of haptic feedback. This paper introduces a Deep Reinforcement Learning-based radio resource slicing framework that addresses video-haptic teleoperation challenges by dynamically balancing radio resources between the video and haptic modalities. The proposed framework employs a refined reward function that considers latency, packet loss, data rate, and the synchronization requirements of both modalities to optimize resource allocation. By catering to the specific service requirements of video-haptic teleoperation, the proposed framework achieves up to a 25% increase in user satisfaction over existing methods, while maintaining effective resource slicing with execution intervals up to 50 ms.
Dynamic Joint Communications and Sensing Precoding Design: A Lyapunov Approach
Authors: Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb, Markku Juntti
Date: 2025-03-18 00:00:00

This letter proposes a dynamic joint communications and sensing (JCAS) framework to adaptively design dedicated sensing and communications precoders. We first formulate a stochastic control problem to maximize the long-term average signal-to-noise ratio for sensing, subject to a minimum average communications signal-to-interference-plus-noise ratio requirement and a power budget. Using Lyapunov optimization, specifically the drift-plus-penalty method, we cast the problem into a sequence of per-slot non-convex problems. To solve these problems, we develop a successive convex approximation method. Additionally, we derive a closed-form solution to the per-slot problems based on the notion of zero-forcing. Numerical evaluations demonstrate the efficacy of the proposed methods and highlight their superiority compared to a baseline method based on conventional design.
Multi-Modal Self-Supervised Semantic Communication
Authors: Hang Zhao, Hongru Li, Dongfang Xu, Shenghui Song, Khaled B. Letaief
Date: 2025-03-18 00:00:00
Tasks: Self-Supervised Learning, Semantic Communication

Semantic communication is emerging as a promising paradigm that focuses on the extraction and transmission of semantic meanings using deep learning techniques. While current research primarily addresses the reduction of semantic communication overhead, it often overlooks the training phase, which can incur significant communication costs in dynamic wireless environments. To address this challenge, we propose a multi-modal semantic communication system that leverages multi-modal self-supervised learning to enhance task-agnostic feature extraction. The proposed approach employs self-supervised learning during the pre-training phase to extract task-agnostic semantic features, followed by supervised fine-tuning for downstream tasks. This dual-phase strategy effectively captures both modality-invariant and modality-specific features while minimizing training-related communication overhead. Experimental results on the NYU Depth V2 dataset demonstrate that the proposed method significantly reduces training-related communication overhead while maintaining or exceeding the performance of existing supervised learning approaches. The findings underscore the advantages of multi-modal self-supervised learning in semantic communication, paving the way for more efficient and scalable edge inference systems.
Joint ADS-B in B5G for Hierarchical UAV Networks: Performance Analysis and MEC Based Optimization
Authors: Chao Dong, Yiyang Liao, Ziye Jia, Qihui Wu, Lei Zhang
Date: 2025-03-18 00:00:00
Tasks: Edge-computing

Unmanned aerial vehicles (UAVs) play significant roles in multiple fields, which brings great challenges for the airspace safety. In order to achieve efficient surveillance and break the limitation of application scenarios caused by single communication, we propose the collaborative surveillance model for hierarchical UAVs based on the cooperation of automatic dependent surveillance-broadcast (ADS-B) and 5G. Specifically, UAVs are hierarchical deployed, with the low-altitude central UAV equipped with the 5G module, and the high-altitude central UAV with ADS-B, which helps automatically broadcast the flight information to surrounding aircraft and ground stations. Firstly, we build the framework, derive the analytic expression, and analyze the channel performance of both air-to-ground (A2G) and air-to-air (A2A). Then, since the redundancy or information loss during transmission aggravates the monitoring performance, the mobile edge computing (MEC) based on-board processing algorithm is proposed. Finally, the performances of the proposed model and algorithm are verified through both simulations and experiments. In detail, the redundant data filtered out by the proposed algorithm accounts for 53.48%, and the supplementary data accounts for 16.42% of the optimized data. This work designs a UAV monitoring framework and proposes an algorithm to enhance the observability of trajectory surveillance, which helps improve the airspace safety and enhance the air traffic flow management.
Optimization and Characterization of Near-Field Beams with Uniform Linear Arrays
Authors: Sota Uchimura, Josep Miquel Jornet, Koji Ishibashi
Date: 2025-03-18 00:00:00

In this paper, we consider near-field beams that can mitigate signal attenuation and blockage effects using a uniform linear array (ULA). In particular, closed-form expressions for phase distributions in a ULA are derived to generate Bessel beams and curving beams based on the desired propagation directions and trajectories. Based on the phase distributions, the maximum steering angle and propagation distance of Bessel beams with a ULA are revealed. In addition, from the sampling theorem in the spatial domain, the requirements for ULAs to properly generate Bessel beams are clarified. For curving beams, trajectories to reach a user while avoiding one obstacle are designed via the Lagrangian method. Numerical results obtained by electromagnetic wave simulations confirm the effectiveness of the analyses for Bessel beams and the curving beam designs. Furthermore, the characteristics of Gaussian beams, beamfocusing, Bessel beams, and curving beams are summarized in terms of the statistical behavior of their intensity and signal processing.
Joint Array Partitioning and Beamforming Designs in ISAC Systems: A Bayesian CRB Perspective
Authors: Rang Liu, Ming Li, A. Lee Swindlehurst
Date: 2025-03-18 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has emerged as a promising paradigm for next-generation (6G) wireless networks, unifying radar sensing and communication on a shared hardware platform. This paper proposes a dynamic array partitioning framework for monostatic ISAC systems to fully exploit available spatial degrees of freedom (DoFs) and reconfigurable antenna topologies, enhancing sensing performance in complex scenarios. We first establish a theoretical foundation for our work by deriving Bayesian Cram\'{e}r-Rao bounds (BCRBs) under prior distribution constraints for heterogeneous target models, encompassing both point-like and extended targets. Building on this, we formulate a joint optimization framework for transmit beamforming and dynamic array partitioning to minimize the derived BCRBs for direction-of-arrival (DOA) estimation. The optimization problem incorporates practical constraints, including multi-user communication signal-to-interference-plus-noise ratio (SINR) requirements, transmit power budgets, and array partitioning feasibility conditions. To address the non-convexity of the problem, we develop an efficient alternating optimization algorithm combining the alternating direction method of multipliers (ADMM) with semi-definite relaxation (SDR). We also design novel maximum a posteriori (MAP) DOA estimation algorithms specifically adapted to the statistical characteristics of each target model. Extensive simulations illustrate the superiority of the proposed dynamic partitioning strategy over conventional fixed-array architectures across diverse system configurations.
Send Pilot or Data? Leveraging Age of Channel State Information for Throughput Maximization
Authors: Sirin Chakraborty, Yin Sun
Date: 2025-03-18 00:00:00
Tasks: Scheduling

In this paper, we study the optimal timing for pilot and data transmissions to maximize effective throughput, also known as goodput, over a wireless fading channel. The receiver utilizes the received pilot signal and its Age of Information (AoI), termed the Age of Channel State Information (AoCSI), to estimate the channel state. Based on this estimation, the transmitter selects an appropriate modulation and coding scheme (MCS) to maximize goodput while ensuring compliance with a predefined block error probability constraint. Furthermore, we design an optimal pilot scheduling policy that determines whether to transmit a pilot or data at each time step, with the objective of maximizing the long-term average goodput. This problem involves a non-monotonic AoI metric optimization challenge, as the goodput function is non-monotonic with respect to AoCSI. The numerical results illustrate the performance gains achieved by the proposed policy under various SNR levels and mobility speeds.
Single Sparse Graph Enhanced Expectation Propagation Algorithm Design for Uplink MIMO-SCMA
Authors: Qu Luo, Jing Zhu, Gaojie Chen, Pei Xiao, Rahim Tafazolli
Date: 2025-03-17 00:00:00

Sparse code multiple access (SCMA) and multiple input multiple output (MIMO) are considered as two efficient techniques to provide both massive connectivity and high spectrum efficiency for future machine-type wireless networks. This paper proposes a single sparse graph (SSG) enhanced expectation propagation algorithm (EPA) receiver, referred to as SSG-EPA, for uplink MIMO-SCMA systems. Firstly, we reformulate the sparse codebook mapping process using a linear encoding model, which transforms the variable nodes (VNs) of SCMA from symbol-level to bit-level VNs. Such transformation facilitates the integration of the VNs of SCMA and low-density parity-check (LDPC), thereby emerging the SCMA and LDPC graphs into a SSG. Subsequently, to further reduce the detection complexity, the message propagation between SCMA VNs and function nodes (FNs) are designed based on EPA principles. Different from the existing iterative detection and decoding (IDD) structure, the proposed EPA-SSG allows a simultaneously detection and decoding at each iteration, and eliminates the use of interleavers, de-interleavers, symbol-to-bit, and bit-to-symbol LLR transformations. Simulation results show that the proposed SSG-EPA achieves better error rate performance compared to the state-of-the-art schemes.
Convolutional neural network for early detection of lameness and irregularity in horses using an IMU sensor
Authors: Benoît Savoini, Jonathan Bertolaccini, Stéphane Montavon, Michel Deriaz
Date: 2025-03-17 00:00:00

Lameness and gait irregularities are significant concerns in equine health management, affecting performance, welfare, and economic value. Traditional observational methods rely on subjective expert assessments, which can lead to inconsistencies in detecting subtle or early-stage lameness. While AI-based approaches have emerged, many require multiple sensors, force plates, or video systems, making them costly and impractical for field deployment. In this applied research study, we present a stride-level classification system that utilizes a single inertial measurement unit (IMU) and a one-dimensional convolutional neural network (1D CNN) to objectively differentiate between sound and lame horses, with a primary focus on the trot gait. The proposed system was tested under real-world conditions, achieving a 90% session-level accuracy with no false positives, demonstrating its robustness for practical applications. By employing a single, non-intrusive, and readily available sensor, our approach significantly reduces the complexity and cost of hardware requirements while maintaining high classification performance. These results highlight the potential of our CNN-based method as a field-tested, scalable solution for automated lameness detection. By enabling early diagnosis, this system offers a valuable tool for preventing minor gait irregularities from developing into severe conditions, ultimately contributing to improved equine welfare and performance in veterinary and equestrian practice.
Highly Efficient Direct Analytics on Semantic-aware Time Series Data Compression
Authors: Guoyou Sun, Panagiotis Karras, Qi Zhang
Date: 2025-03-17 00:00:00
Tasks: Data Compression, Outlier Detection, Semantic Communication, Time Series

Semantic communication has emerged as a promising paradigm to tackle the challenges of massive growing data traffic and sustainable data communication. It shifts the focus from data fidelity to goal-oriented or task-oriented semantic transmission. While deep learning-based methods are commonly used for semantic encoding and decoding, they struggle with the sequential nature of time series data and high computation cost, particularly in resource-constrained IoT environments. Data compression plays a crucial role in reducing transmission and storage costs, yet traditional data compression methods fall short of the demands of goal-oriented communication systems. In this paper, we propose a novel method for direct analytics on time series data compressed by the SHRINK compression algorithm. Through experimentation using outlier detection as a case study, we show that our method outperforms baselines running on uncompressed data in multiple cases, with merely 1% difference in the worst case. Additionally, it achieves four times lower runtime on average and accesses approximately 10% of the data volume, which enables edge analytics with limited storage and computation power. These results demonstrate that our approach offers reliable, high-speed outlier detection analytics for diverse IoT applications while extracting semantics from time-series data, achieving high compression, and reducing data transmission.
Equalization-Enhanced Phase Noise: Modeling and DSP-aware Analysis
Authors: Sebastian Jung, Tim Janz, Vahid Aref, Stephan ten Brink
Date: 2025-03-17 00:00:00

In coherent optical communication systems the laser phase noise is commonly modeled as a Wiener process. We propose a sliding-window based linearization of the phase noise, enabling a novel description. We show that, by stochastically modeling the residual error introduced by this approximation, equalization-enhanced phase noise (EEPN) can be described and decomposed into four different components. Furthermore, we analyze the four components separately and provide a stochastical model for each of them. This novel model allows to predict the impact of well-known algorithms in coherent digital signal processing (DSP) pipelines such as timing recovery (TR) and carrier phase recovery (CPR) on each of the terms. Thus, it enables to approximate the resulting signal affected by EEPN after each of these DSP steps and helps to derive appropriate ways of mitigating such effects.