pwc-archive/papers-with-abstracts huggingface dataset:
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
Authors: Ori Peleg, Natalie Lang, Stefano Rini, Nir Shlezinger, Kobi Cohen
Date: 2025-03-17 00:00:00
Tasks: Federated Learning

Federated learning (FL) enables multiple edge devices to collaboratively train a machine learning model without the need to share potentially private data. Federated learning proceeds through iterative exchanges of model updates, which pose two key challenges: First, the accumulation of privacy leakage over time, and second, communication latency. These two limitations are typically addressed separately: The former via perturbed updates to enhance privacy and the latter using user selection to mitigate latency - both at the expense of accuracy. In this work, we propose a method that jointly addresses the accumulation of privacy leakage and communication latency via active user selection, aiming to improve the trade-off among privacy, latency, and model performance. To achieve this, we construct a reward function that accounts for these three objectives. Building on this reward, we propose a multi-armed bandit (MAB)-based algorithm, termed Privacy-aware Active User SElection (PAUSE) which dynamically selects a subset of users each round while ensuring bounded overall privacy leakage. We establish a theoretical analysis, systematically showing that the reward growth rate of PAUSE follows that of the best-known rate in MAB literature. To address the complexity overhead of active user selection, we propose a simulated annealing-based relaxation of PAUSE and analyze its ability to approximate the reward-maximizing policy under reduced complexity. We numerically validate the privacy leakage, associated improved latency, and accuracy gains of our methods for the federated training in various scenarios.
A Design of Denser-Graph-Frequency Graph Fourier Frames for Graph Signal Analysis
Authors: Kaito Nitani, Seisuke Kyochi
Date: 2025-03-17 00:00:00
Tasks: Denoising

This paper introduces a design method for densergraph-frequency graph Fourier frames (DGFFs) to enhance graph signal processing and analysis. The graph Fourier transform (GFT) enables us to analyze graph signals in the graph spectral domain and facilitates various graph signal processing tasks, such as filtering, sampling and reconstruction, denoising, and so on. However, the conventional GFT faces two significant limitations. First, unlike the discrete Fourier transform and its variants (such as discrete cosine transforms), the graph frequencies of the derived graph Fourier basis (GFB) from a given graph tend to be unevenly distributed or localized, which leads to biased spectral analysis. Second, the GFB used in GFT does not provide an efficient sparse representation of graph signals compared to overcomplete systems like frames. To overcome these challenges, we propose adding oscillating vectors with intermediate graph frequencies between the original vectors in the GFB for both undirected and directed graphs, constructing GFFs with densergraph frequencies. The resulting DGFFs are expected to enable more accurate graph signal analysis. Furthermore, we propose a graph filtering method based on the DGFFs. In experiments, we apply the DGFFs to practical applications such as graph signal recovery, demonstrating superior performance compared to existing GFBs.
Moment-based Characterization of Spatially Distributed Sources in SAR Tomography
Authors: Colin Cros, Laurent Ferro-Famil
Date: 2025-03-17 00:00:00

This paper presents a non-parametric method for 3-D imaging of natural volumes using Synthetic Aperture Radar tomography. This array processing-based technique aims at characterizing a spatially distributed density of incoherent sources, whose shape is imprecisely known. The proposed technique estimates the moments of the reflectivity density using a low-complexity covariance matching approach, and retrieves the mean location, dispersion, and power of the distributed source. Numerical simulations of realistic tomographic scenarios show that the proposed model-free scheme achieves better accuracy than slightly misspecified maximum likelihood estimators, derived from approximately known distribution shapes.
Goal-Oriented Remote Tracking Through Correlated Observations in Pull-based Communications
Authors: Abolfazl Zakeri, Mohammad Moltafet, Marian Codreanu
Date: 2025-03-17 00:00:00

We address the real-time remote tracking problem in a status update system comprising two sensors, two independent information sources, and a remote monitor. The status updating follows a pull-based communication, where the monitor commands/pulls the sensors for status updates, i.e., the actual state of the sources. We consider that the observations are correlated, meaning that each sensor sent data could also include the state of the other source due to, e.g., inter-sensor communication or proximity-based monitoring. The effectiveness of data communication is measured by a generic distortion, capturing the underlying application goal. We provide optimal command/pulling policies for the monitor that minimize the average weighted sum distortion and transmission cost. Since the monitor cannot fully observe the exact state of each source, we propose a partially observable Markov decision process (POMDP) and reformulate it as a belief MDP problem. We then effectively truncate the infinite belief space and transform it into a finite-state MDP problem, which is solved via relative value iteration. Simulation results show the effectiveness of the derived policy over the age-optimal and max-age-first baseline policies.
Classification of power quality events in the transmission grid: comparative evaluation of different machine learning models
Authors: Umut Güvengir, Dilek Küçük, Serkan Buhan, Cuma Ali Mantaş, Murathan Yeniceli
Date: 2025-03-17 00:00:00
Tasks: Classification, Management

Automatic classification of electric power quality events with respect to their root causes is critical for electrical grid management. In this paper, we present comparative evaluation results of an extensive set of machine learning models for the classification of power quality events, based on their root causes. After extensive experiments using different machine learning libraries, it is observed that the best performing learning models turn out to be Cubic SVM and XGBoost. During error analysis, it is observed that the main source of performance degradation for both models is the classification of ABC faults as ABCG faults, or vice versa. Ultimately, the models achieving the best results will be integrated into the event classification module of a large-scale power quality and grid monitoring system for the Turkish electricity transmission system.
Task-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inference
Authors: Cheng Yuan, Zhening Liu, Jiashu Lv, Jiawei Shao, Yufei Jiang, Jun Zhang, Xuelong Li
Date: 2025-03-17 00:00:00
Tasks: Feature Compression, Image Compression, Question Answering, Visual Question Answering

With the rapid development of large multimodal models (LMMs), multimodal understanding applications are emerging. As most LMM inference requests originate from edge devices with limited computational capabilities, the predominant inference pipeline involves directly forwarding the input data to an edge server which handles all computations. However, this approach introduces high transmission latency due to limited uplink bandwidth of edge devices and significant computation latency caused by the prohibitive number of visual tokens, thus hindering delay-sensitive tasks and degrading user experience. To address this challenge, we propose a task-oriented feature compression (TOFC) method for multimodal understanding in a device-edge co-inference framework, where visual features are merged by clustering and encoded by a learnable and selective entropy model before feature projection. Specifically, we employ density peaks clustering based on K nearest neighbors to reduce the number of visual features, thereby minimizing both data transmission and computational complexity. Subsequently, a learnable entropy model with hyperprior is utilized to encode and decode merged features, further reducing transmission overhead. To enhance compression efficiency, multiple entropy models are adaptively selected based on the characteristics of the visual features, enabling a more accurate estimation of the probability distribution. Comprehensive experiments on seven visual question answering benchmarks validate the effectiveness of the proposed TOFC method. Results show that TOFC achieves up to 60% reduction in data transmission overhead and 50% reduction in system latency while maintaining identical task performance, compared with traditional image compression methods.
A Block-Sparse Bayesian Learning Algorithm with Dictionary Parameter Estimation for Multi-Sensor Data Fusion
Authors: Jakob Möderl, Anders Malte Westerkam, Alexander Venus, Erik Leitinger
Date: 2025-03-17 00:00:00
Tasks: parameter estimation

We propose an sparse Bayesian learning (SBL)-based method that leverages group sparsity and multiple parameterized dictionaries to detect the relevant dictionary entries and estimate their continuous parameters by combining data from multiple independent sensors. In a MIMO multi-radar setup, we demonstrate its effectiveness in jointly detecting and localizing multiple objects, while also emphasizing its broader applicability to various signal processing tasks. A key benefit of the proposed SBL-based method is its ability to resolve correlated dictionary entries-such as closely spaced objects-resulting in uncorrelated estimates that improve subsequent estimation stages. Through numerical simulations, we show that our method outperforms the newtonized orthogonal matching pursuit (NOMP) algorithm when two objects cross paths using a single radar. Furthermore, we illustrate how fusing measurements from multiple independent radars leads to enhanced detection and localization performance
High-Resolution Range-Doppler Imaging from One-Bit PMCW Radar via Generative Adversarial Networks
Authors: Jingxian Wang, Moritz Kahlert, Tai Fei, Changxu Zhang, Zhaoze Wang, Markus Gardill
Date: 2025-03-17 00:00:00

Digital modulation schemes such as PMCW have recently attracted increasing attention as possible replacements for FMCW modulation in future automotive radar systems. A significant obstacle to their widespread adoption is the expensive and power-consuming ADC required at gigahertz frequencies. To mitigate these challenges, employing low-resolution ADC, such as one-bit, has been suggested. Nonetheless, using one-bit sampling results in the loss of essential information. This study explores two RD imaging methods in PMCW radar systems utilizing NN. The first method merges standard RD signal processing with a GAN, whereas the second method uses an E2E strategy in which traditional signal processing is substituted with an NN-based RD module. The findings indicate that these methods can substantially improve the probability of detecting targets in the range-Doppler domain.
Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life
Authors: Jingyuan Xue, Longfei Wei, Dongjing Jiang, Fang Sheng, Russell Greiner, Jianfei Zhang
Date: 2025-03-17 00:00:00
Tasks: Survival Analysis, Uncertainty Quantification

Battery degradation significantly impacts the reliability and efficiency of energy storage systems, particularly in electric vehicles and industrial applications. Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for optimizing maintenance schedules, reducing costs, and improving safety. Traditional RUL prediction methods often struggle with nonlinear degradation patterns and uncertainty quantification. To address these challenges, we propose a hybrid survival analysis framework integrating survival data reconstruction, survival model learning, and survival probability estimation. Our approach transforms battery voltage time series into time-to-failure data using path signatures. The multiple Cox-based survival models and machine-learning-based methods, such as DeepHit and MTLR, are learned to predict battery failure-free probabilities over time. Experiments conducted on the Toyota battery and NASA battery datasets demonstrate the effectiveness of our approach, achieving high time-dependent AUC and concordance index (C-Index) while maintaining a low integrated Brier score. The data and source codes for this work are available to the public at https://github.com/thinkxca/rul.
Redefining Orthogonal Co-Existence: A Mother Waveform Framework for DFT-Based Waveforms
Authors: Ayoub Ammar Boudjelal, Rania Yasmine Bir, Huseyin Arslan
Date: 2025-03-16 00:00:00

In this paper, we introduce the concept of a mother waveform to address key challenges in 5th generation (5G) and 6th generation (6G) networks, including spectral efficiency, backward compatibility, enhanced flexibility, and the integration of joint sensing and communication (JSAC). We propose single-carrier interleaved frequency division multiplexing (SC-IFDM) as the mother waveform and demonstrate, through rigorous mathematical modeling, that it can generate all discrete Fourier transform (DFT)-based waveforms without requiring structural modifications. Specifically, by selectively configuring lattice indices and phase adjustments, SC-IFDM enables seamless adaptation to diverse waveforms, such as orthogonal frequency division multiplexing (OFDM), orthogonal chirp division multiplexing (OCDM), orthogonal time-frequency space (OTFS), affine frequency division multiplexing (AFDM), and frequency-modulated continuous wave (FMCW) within a unified framework. Critical aspects such as coexistence strategies and resource allocation are thoroughly explored. Simulation results demonstrate the proposed frameworks ability to deliver superior communication performance, robust sensing capabilities, and efficient coexistence, surpassing traditional waveform designs in scalability and adaptability.
TEANet: A Transpose-Enhanced Autoencoder Network for Wearable Stress Monitoring
Authors: Md Santo Ali, Sapnil Sarker Bipro, Mohammod Abdul Motin, Sumaiya Kabir, Manish Sharma, M. E. H. Chowdhury
Date: 2025-03-16 00:00:00

Mental stress poses a significant public health concern due to its detrimental effects on physical and mental well-being, necessitating the development of continuous stress monitoring tools for wearable devices. Blood volume pulse (BVP) sensors, readily available in many smartwatches, offer a convenient and cost-effective solution for stress monitoring. This study proposes a deep learning approach, a Transpose-Enhanced Autoencoder Network (TEANet), for stress detection using BVP signals. The proposed TEANet model was trained and validated utilizing a self-collected RUET SPML dataset, comprising 19 healthy subjects, and the publicly available wearable stress and affect detection (WESAD) dataset, comprising 15 healthy subjects. It achieves the highest accuracy of 92.51% and 96.94%, F1 scores of 95.03% and 95.95%, and kappa of 0.7915 and 0.9350 for RUET SPML, and WESAD datasets respectively. The proposed TEANet effectively detects mental stress through BVP signals with high accuracy, making it a promising tool for continuous stress monitoring. Furthermore, the proposed model effectively addresses class imbalances and demonstrates high accuracy, underscoring its potential for reliable real-time stress monitoring using wearable devices.
Optimal Transmission Sequence Design with ISI Matching in Molecular Communication
Authors: Aravind Muraleedharan, Abhishek K. Gupta
Date: 2025-03-16 00:00:00

Molecular communication (MC) offers a groundbreaking approach to communication inspired by biological signaling. It is particularly suited for environments where traditional electromagnetic methods fail, such as fluid mediums or within the human body. This study focuses on addressing a major challenge in MC systems: inter symbol interference (ISI), which arises due to the random, diffusive propagation of molecules. We propose a novel technique that leverages transmission shaping to mitigate ISI effectively by designing optimal transmission pulse (or sequence) for symbols. Our approach centers on solving a multi-objective optimization problem that aims to maximize the separability of individual symbol's responses within the symbol duration while matching the interference caused by molecular spillover for all symbols. By making ISI of each symbol similar, the approach reduces the effect of previous symbols and thus not require any adaptive computations. We introduce a geometric analogy involving two families of ellipses to derive the optimal solution. Analytical insights are supported by numerical simulations to design optimized transmission profiles to enhance the resilience toward ISI. The proposed transmission shaping method is evaluated through symbol error rate (SER). These results mark a significant step forward in developing robust and efficient MC systems, opening doors to advanced applications in bio-inspired and nano-scale communication technologies.
Single-Carrier Waveform Design for Joint Sensing and Communication
Authors: Ayoub Ammar Boudjelal, Rania Yasmine Bir, Huseyin Arslan
Date: 2025-03-16 00:00:00

The emergence of 6G wireless networks demands solutions that seamlessly integrate communication and sensing. This letter proposes a novel waveform design for joint sensing and communication (JSAC) systems, combining single-carrier interleaved frequency division multiplexing (SC-IFDM), a 5G communication candidate signal, with frequency modulated continuous wave (FMCW), widely used for sensing. The proposed waveform leverages the sparse nature of FMCW within SC-IFDM to achieve orthogonal integration in three steps: SC-IFDM symbols are allocated alongside the sparse FMCW, followed by the SC-IFDM transform into the time domain, and a cyclic prefix (CP) is applied in which phase shifts are introduced to the FMCW. Additionally, an enhanced channel estimation method is incorporated to boost system performance. Simulation results demonstrate the proposed waveform's ability to deliver high-resolution sensing and superior communication performance, surpassing traditional multicarrier designs.
Limited-Resolution Hybrid Analog-Digital Precoding Design Using MIMO Detection Methods
Authors: Parisa Ramezani, Alva Kosasih, Emil Björnson
Date: 2025-03-16 00:00:00

While fully digital precoding achieves superior performance in massive multiple-input multiple-output (MIMO) systems, it comes with significant drawbacks in terms of computational complexity and power consumption, particularly when operating at millimeter-wave frequencies and beyond. Hybrid analog-digital architectures address this by reducing radio frequency (RF) chains while maintaining performance in sparse multipath environments. However, most hybrid precoder designs assume ideal, infinite-resolution analog phase shifters, which cannot be implemented in actual systems. Another practical constraint is the limited fronthaul capacity between the baseband processor and array, implying that each entry of the digital precoder must be picked from a finite set of quantization labels. This paper proposes novel designs for the limited-resolution analog and digital precoders by exploiting two well-known MIMO symbol detection algorithms, namely sphere decoding (SD) and expectation propagation (EP). The goal is to minimize the Euclidean distance between the optimal fully digital precoder and the hybrid precoder to minimize the degradation caused by the finite resolution of the analog and digital precoders. Taking an alternative optimization approach, we first apply the SD method to find the precoders in each iteration optimally. Then, we apply the lower-complexity EP method which finds a near-optimal solution at a reduced computational cost. The effectiveness of the proposed designs is validated via numerical simulations, where we show that the proposed symbol detection-based precoder designs significantly outperform the nearest point mapping scheme which is commonly used for finding a sub-optimal solution to discrete optimization problems.
Fuzzy Clustering for Low-Complexity Time Domain Chromatic Dispersion Compensation Scheme in Coherent Optical Fiber Communication Systems
Authors: Wenkai Wan, Aiying Yang, Peng Guo, Zhe Zhao, Tianjia Xu, Jinxuan Wu, Zhiheng Liu
Date: 2025-03-16 00:00:00

Chromatic dispersion compensation (CDC), implemented in either the time-domain or frequency-domain, is crucial for enhancing power efficiency in the digital signal processing of modern optical fiber communication systems. Developing low-complexity CDC schemes is essential for hardware implemention, particularly for high-speed and long-haul optical fiber communication systems. In this work, we propose a novel two-stage fuzzy clustered time-domain chromatic dispersion compensation scheme. Unlike hard decisions of CDC filter coefficients after determining the cluster centroids, our approach applies a soft fuzzy decision, allowing the coefficients to belong to multiple clusters. Experiments on a single-channel, single-polarization 20Gbaud 16-QAM 1800 km standard single-mode fiber communication system demonstrate that our approach has a complexity reduction of 53.8% and 40% compared with clustered TD-CDC and FD-CDC at a target Q-factor of 20% HD-FEC, respectively. Furthermore, the proposed method achieves the same optimal Q-factor as FD-CDC with a 27% complexity reduction.
Movable Cell-Free Massive MIMO For High-Speed Train Communications: A PPO-Based Antenna Position Optimization
Authors: Jie Dai, Yuchen Liu, Jiakang Zheng, Ruichen Zhang, Jiayi Zhang, Bo Ai
Date: 2025-03-16 00:00:00
Tasks: Deep Reinforcement Learning, Position

In recent years, high-speed trains (HSTs) communications have developed rapidly to enhance the stability of train operations and improve passenger connectivity experiences. However, as the train continues to accelerate, urgent technological innovations are needed to overcome challenges such as frequency handover and significant Doppler effects. In this paper, we present a novel architecture featuring movable antennas (MAs) to fully exploit macro spatial diversity, enabling a cell-free (CF) massive multiple-input multiple-output (MIMO) system that supports high-speed train communications. Considering the high likelihood of line-of-sight (LoS) transmission in HST scenario, we derive the uplink spectral efficiency (SE) expression for the movable CF massive MIMO system. Moreover, an optimization problem is formulated to maximize the sum SE of the considered system by optimizing the positions of the antennas. Since the formulated problem is non-convex and highly non-linear, we improve a deep reinforcement learning algorithm to address it by using proximal policy optimization (PPO). Different from traditional optimization approaches, which optimize variables separately and alternately, our improved PPO-based approach optimizes all the variables in unison. Simulation results demonstrate that movable CF massive MIMO effectively suppresses the negative impact of the Doppler effect in HST communications.