pwc-archive/papers-with-abstracts huggingface dataset:
Array Partitioning Based Near-Field Attitude and Location Estimation
Authors: Mingchen Zhang, Xiaojun Yuan, Boyu Teng, Li Wang
Date: 2025-04-23 00:00:00
Tasks: Bayesian Inference

This paper studies a passive source localization system, where a single base station (BS) is employed to estimate the positions and attitudes of multiple mobile stations (MSs). The BS and the MSs are equipped with uniform rectangular arrays, and the MSs are located in the near-field region of the BS array. To avoid the difficulty of tackling the problem directly based on the near-field signal model, we establish a subarray-wise far-field received signal model. In this model, the entire BS array is divided into multiple subarrays to ensure that each MS is in the far-field region of each BS subarray. By exploiting the angles of arrival (AoAs) of an MS antenna at different BS subarrays, we formulate the attitude and location estimation problem under the Bayesian inference framework. Based on the factor graph representation of the probabilistic problem model, a message passing algorithm named array partitioning based pose and location estimation (APPLE) is developed to solve this problem. An estimation-error lower bound is obtained as a performance benchmark of the proposed algorithm. Numerical results demonstrate that the proposed APPLE algorithm outperforms other baseline methods in the accuracy of position and attitude estimation.
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications
Authors: Hossein Ahmadi, Sajjad Emdadi Mahdimahalleh, Arman Farahat, Banafsheh Saffari
Date: 2025-04-23 00:00:00
Tasks: Anomaly Detection, Domain Generalization, Self-Supervised Learning, Time Series

The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised learning. This review synthesizes recent progress in applying autoencoders and vision transformers for unsupervised signal analysis, focusing on their architectures, applications, and emerging trends. We explore how these models enable feature extraction, anomaly detection, and classification across diverse signal types, including electrocardiograms, radar waveforms, and IoT sensor data. The review highlights the strengths of hybrid architectures and self-supervised learning, while identifying challenges in interpretability, scalability, and domain generalization. By bridging methodological innovations and practical applications, this work offers a roadmap for developing robust, adaptive models for signal intelligence.
On the Asymptotic MSE-Optimality of Parametric Bayesian Channel Estimation in mmWave Systems
Authors: Franz Weißer, Wolfgang Utschick
Date: 2025-04-23 00:00:00

The mean square error (MSE)-optimal estimator is known to be the conditional mean estimator (CME). This paper introduces a parametric channel estimation technique based on Bayesian estimation. This technique uses the estimated channel parameters to parameterize the well-known LMMSE channel estimator. We first derive an asymptotic CME formulation that holds for a wide range of priors on the channel parameters. Based on this, we show that parametric Bayesian channel estimation is MSE-optimal for high signal-to-noise ratio (SNR) and/or long coherence intervals, i.e., many noisy observations provided within one coherence interval. Numerical simulations validate the derived formulations.
A Novel Sparse Sum and Difference Co-Array With Low Redundancy and Enhanced DOF for Non-Circular Signals
Authors: Si Wang, GuoQiang Xiao
Date: 2025-04-23 00:00:00

Array structures based on the sum and difference co-arrays provide more degrees of freedom (DOF). However, since the growth of DOF is limited by a single case of sum and difference co-arrays, the paper aims to design a sparse linear array (SLA) with higher DOF via exploring different cases of second-order cumulants. We present a mathematical framework based on second-order cumulant to devise a second-order extended co-array (SO-ECA) and define the redundancy of SO-ECA. Based on SO-ECA, a novel array is proposed, namely low redundancy sum and difference array (LR-SDA), which can provide closed-form expressions for the sensor positions and enhance DOF in order to resolve more signal sources in the direction of arrival (DOA) estimation of non-circular (NC) signals. For LR-SDA, the maximum DOF under the given number of total physical sensors can be derived and the SO-ECA of LR-SDA is hole-free. Further, the corresponding necessary and sufficient conditions of signal reconstruction for LR-SDA are derived. Additionally, the redundancy and weight function of LR-SDA are defined, and the lower band of the redundancy for LR-SDA is derived. The proposed LR-SDA achieves higher DOF and lower redundancy than those of existing DCAs designed based on sum and difference co-arrays. Numerical simulations are conducted to verify the superiority of LR-SDA on DOA estimation performance and enhanced DOF over other existing DCAs.
Performance Assessment of Hybrid and Digital Irregular Array Configurations for Beyond 100-GHz Multi-User MIMO Systems
Authors: Yigit Ertugrul, Kamil Yavuz Kapusuz, Claude Desset, Sofie Pollin
Date: 2025-04-23 00:00:00

The performance of irregular phased array architectures is assessed in the context of multi-user multiple-input multiple-output (MU-MIMO) communications operating beyond 100 GHz. Realizing half-wavelength spaced planar phased arrays is challenging due to wavelength-integrated circuit (IC) size conflict at those frequencies where the antenna dimensions are comparable to IC size. Therefore, irregular array architectures such as thinned and clustered arrays are developed to mitigate the wavelength-IC size conflict. In the thinned arrays, radiating elements are permanently deactivated, while in clustered arrays, neighboring elements are grouped into subarrays. Furthermore, irregular arrays are integrated with hybrid beamforming architectures to manage the complexity introduced by full digital beamforming, where a single radio frequency chain is connected per power amplifier. An optimization problem is formulated to determine the optimal arrangement of antenna elements where the trade-off between spectral efficiency (SE) and sidelobe levels (SLL) can be tuned. Clustered array configurations are optimized by genetic algorithm and Algorithm-X based methodologies, where the former relies on a randomized search and the latter exploits brute-force search, respectively. Furthermore, a prototype array is designed on a printed circuit board (PCB) to verify the proposed methodologies through full-wave simulations. To have a fair comparison, clustered arrays with a grouping of two and four elements are compared with thinned arrays with half and quarter thinning ratios, respectively. The combination of hybrid and irregular array architectures leads to minimal or no performance degradation in the case of hybrid fully connected architectures but severe SE and SLL degradation in the case of hybrid partially connected architectures, respectively.
Joint Topology and Power Optimization for Multi-UAV Collaborative Secure Communication
Authors: Bin Qiu, Wenchi Cheng, Hongxiang He, Wei zhang
Date: 2025-04-23 00:00:00

In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled secure communication scenario that a cluster of UAVs performs a virtual non-uniform linear array (NULA) to communicate with a base station (BS) in the presence of eavesdroppers (Eves). Our goal is to design the UAV topology, trajectory, and precoding to maximize the system channel capacity. To this end, we convert the original problem into equivalent two-stage problems. Specifically, we first try to maximize the channel gain by meticulously designing the UAV topology. We then study the joint optimization of the trajectory and precoding for total transmit power minimization while satisfying the constraints on providing quality of service (QoS) assurance to the BS, the leakage tolerance to Eves, the per-UAV transmit power, the initial/final locations, and the cylindrical no-fly zones. For the UAV topology design, we prove that the topology follows the Fekete-point distribution. The design of trajectory and precoding is formulated as a non-convex optimization problem which is generally intractable. Subsequently, the non-convex constraints are converted into convex terms, and a double-loop search algorithm is proposed to solve the transmit power minimization problem. Introduce random rotation offsets so as to perform a dynamic stochastic channel to enhance the security. Numerical results demonstrate the superiority of the proposed method in promoting capacity.
Robust Transmission Design for Reconfigurable Intelligent Surface and Movable Antenna Enabled Symbiotic Radio Communications
Authors: Bin Lyu, Hao liu, Meng Hua, Wenqing Hong, Shimin Gong, Feng Tian, Abbas Jamalipour
Date: 2025-04-23 00:00:00

This paper explores the application of movable antenna (MA), a cutting-edge technology with the capability of altering antenna positions, in a symbiotic radio (SR) system enabled by reconfigurable intelligent surface (RIS). The goal is to fully exploit the capabilities of both MA and RIS, constructing a better transmission environment for the co-existing primary and secondary transmission systems. For both parasitic SR (PSR) and commensal SR (CSR) scenarios with the channel uncertainties experienced by all transmission links, we design a robust transmission scheme with the goal of maximizing the primary rate while ensuring the secondary transmission quality. To address the maximization problem with thorny non-convex characteristics, we propose an alternating optimization framework that utilizes the General S-Procedure, General Sign-Definiteness Principle, successive convex approximation (SCA), and simulated annealing (SA) improved particle swarm optimization (SA-PSO) algorithms. Numerical results validate that the CSR scenario significantly outperforms the PSR scenario in terms of primary rate, and also show that compared to the fixed-position antenna scheme, the proposed MA scheme can increase the primary rate by 1.62 bps/Hz and 2.37 bps/Hz for the PSR and CSR scenarios, respectively.
Self-Nomination: Deep Learning for Decentralized CSI Feedback Reduction in MU-MIMO Systems
Authors: Juseong Park, Foad Sohrabi, Jinfeng Du, Jeffrey G. Andrews
Date: 2025-04-23 00:00:00
Tasks: Fairness, Scheduling

This paper introduces a novel deep learning-based user-side feedback reduction framework, termed self-nomination. The goal of self-nomination is to reduce the number of users (UEs) feeding back channel state information (CSI) to the base station (BS), by letting each UE decide whether to feed back based on its estimated likelihood of being scheduled and its potential contribution to precoding in a multiuser MIMO (MU-MIMO) downlink. Unlike SNR- or SINR-based thresholding methods, the proposed approach uses rich spatial channel statistics and learns nontrivial correlation effects that affect eventual MU-MIMO scheduling decisions. To train the self-nomination network under an average feedback constraint, we propose two different strategies: one based on direct optimization with gradient approximations, and another using policy gradient-based optimization with a stochastic Bernoulli policy to handle non-differentiable scheduling. The framework also supports proportional-fair scheduling by incorporating dynamic user weights. Numerical results confirm that the proposed self-nomination method significantly reduces CSI feedback overhead. Compared to baseline feedback methods, self-nomination can reduce feedback by as much as 65%, saving not only bandwidth but also allowing many UEs to avoid feedback altogether (and thus, potentially enter a sleep mode). Self-nomination achieves this significant savings with negligible reduction in sum-rate or fairness.
Robust Resource Allocation for Over-the-Air Computation Networks with Fluid Antenna Array
Authors: Saeid Pakravan, Mohsen Ahmadzadeh, Ming Zeng, Ghosheh Abed Hodtani, Jean-Yves Chouinard, Leslie A Rusch
Date: 2025-04-22 00:00:00

Fluid antenna (FA) array is envisioned as a promising technology for next-generation communication systems, owing to its ability to dynamically control the antenna locations. In this paper, we apply FA array to boost the performance of over-the-air computation networks. Given that channel uncertainty will impact negatively not only the beamforming design but also the antenna location optimization, robust resource allocation is performed to minimize the mean squared error of transmitted messages. Block coordinate descent is adopted to decompose the formulated non-convex problem into three subproblems, which are iteratively solved until convergence. Numerical results show the benefits of FA array and the necessity of robust resource allocation under channel uncertainty.
A Thin Flexible Acoustic Transducer with piezoelectric-actuated microdomes for Underwater Communication
Authors: Rong Fu, Xinyu Zhang, Cheng-Hao Yu, Kai Liu, Tauhidul Haque, Leixin Ouyang, Mark Ming-Cheng Cheng
Date: 2025-04-22 00:00:00

This paper presents a flexible thin-film underwater transducer based on a mesoporous PVDF membrane embedded with piezoelectrical-actuated microdomes. To enhance piezoelectric performance, ZnO nanoparticles were used as a sacrificial template to fabricate a sponge-like PVDF structure with increased \b{eta}-phase content and improved mechanical compliance. The device was modeled using finite element analysis and optimized through parametric studies of dome geometry, film thickness, and dome size. Acoustic performance was evaluated through underwater testing, demonstrating high SPL output and reliable data transmission even at low drive voltages. The proposed transducer offers a lightweight, low-cost, and energy-efficient solution for short-range underwater communication in next-generation Ocean IoT systems.
DERD-Net: Learning Depth from Event-based Ray Densities
Authors: Diego de Oliveira Hitzges, Suman Ghosh, Guillermo Gallego
Date: 2025-04-22 00:00:00
Tasks: Depth Estimation, Depth Prediction, Simultaneous Localization and Mapping, Stereo Depth Estimation

Event cameras offer a promising avenue for multi-view stereo depth estimation and Simultaneous Localization And Mapping (SLAM) due to their ability to detect blur-free 3D edges at high-speed and over broad illumination conditions. However, traditional deep learning frameworks designed for conventional cameras struggle with the asynchronous, stream-like nature of event data, as their architectures are optimized for discrete, image-like inputs. We propose a scalable, flexible and adaptable framework for pixel-wise depth estimation with event cameras in both monocular and stereo setups. The 3D scene structure is encoded into disparity space images (DSIs), representing spatial densities of rays obtained by back-projecting events into space via known camera poses. Our neural network processes local subregions of the DSIs combining 3D convolutions and a recurrent structure to recognize valuable patterns for depth prediction. Local processing enables fast inference with full parallelization and ensures constant ultra-low model complexity and memory costs, regardless of camera resolution. Experiments on standard benchmarks (MVSEC and DSEC datasets) demonstrate unprecedented effectiveness: (i) using purely monocular data, our method achieves comparable results to existing stereo methods; (ii) when applied to stereo data, it strongly outperforms all state-of-the-art (SOTA) approaches, reducing the mean absolute error by at least 42%; (iii) our method also allows for increases in depth completeness by more than 3-fold while still yielding a reduction in median absolute error of at least 30%. Given its remarkable performance and effective processing of event-data, our framework holds strong potential to become a standard approach for using deep learning for event-based depth estimation and SLAM. Project page: https://github.com/tub-rip/DERD-Net
Radar Code Design for the Joint Optimization of Detection Performance and Measurement Accuracy in Track Maintenance
Authors: Tao Fan, Augusto Aubry, Vincenzo Carotenuto, Antonio De Maio, Xianxiang Yu, Guolong Cui
Date: 2025-04-21 00:00:00

This paper deals with the design of slow-time coded waveforms which jointly optimize the detection probability and the measurements accuracy for track maintenance in the presence of colored Gaussian interference. The output signal-to-interference-plus-noise ratio (SINR) and Cram\'er Rao bounds (CRBs) on time delay and Doppler shift are used as figures of merit to accomplish reliable detection as well as accurate measurements. The transmitted code is subject to radar power budget requirements and a similarity constraint. To tackle the resulting non-convex multi-objective optimization problem, a polynomial-time algorithm that integrates scalarization and tensor-based relaxation methods is developed. The corresponding relaxed multi-linear problems are solved by means of the maximum block improvement (MBI) framework, where the optimal solution at each iteration is obtained in closed form. Numeral results demonstrate the trade-off between the detection and the estimation performance, along with the acceptable Doppler robustness achieved by the proposed algorithm.
A Statistical Approach for Synthetic EEG Data Generation
Authors: Gideon Vos, Maryam Ebrahimpour, Liza van Eijk, Zoltan Sarnyai, Mostafa Rahimi Azghadi
Date: 2025-04-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Privacy Preserving, Synthetic Data Generation

Electroencephalogram (EEG) data is crucial for diagnosing mental health conditions but is costly and time-consuming to collect at scale. Synthetic data generation offers a promising solution to augment datasets for machine learning applications. However, generating high-quality synthetic EEG that preserves emotional and mental health signals remains challenging. This study proposes a method combining correlation analysis and random sampling to generate realistic synthetic EEG data. We first analyze interdependencies between EEG frequency bands using correlation analysis. Guided by this structure, we generate synthetic samples via random sampling. Samples with high correlation to real data are retained and evaluated through distribution analysis and classification tasks. A Random Forest model trained to distinguish synthetic from real EEG performs at chance level, indicating high fidelity. The generated synthetic data closely match the statistical and structural properties of the original EEG, with similar correlation coefficients and no significant differences in PERMANOVA tests. This method provides a scalable, privacy-preserving approach for augmenting EEG datasets, enabling more efficient model training in mental health research.
A Non-Invasive Load Monitoring Method for Edge Computing Based on MobileNetV3 and Dynamic Time Regulation
Authors: Hangxu Liu, Yaojie Sun, Yu Wang
Date: 2025-04-22 00:00:00
Tasks: Dynamic Time Warping, Edge-computing, Non-Intrusive Load Monitoring

In recent years, non-intrusive load monitoring (NILM) technology has attracted much attention in the related research field by virtue of its unique advantage of utilizing single meter data to achieve accurate decomposition of device-level energy consumption. Cutting-edge methods based on machine learning and deep learning have achieved remarkable results in load decomposition accuracy by fusing time-frequency domain features. However, these methods generally suffer from high computational costs and huge memory requirements, which become the main obstacles for their deployment on resource-constrained microcontroller units (MCUs). To address these challenges, this study proposes an innovative Dynamic Time Warping (DTW) algorithm in the time-frequency domain and systematically compares and analyzes the performance of six machine learning techniques in home electricity scenarios. Through complete experimental validation on edge MCUs, this scheme successfully achieves a recognition accuracy of 95%. Meanwhile, this study deeply optimizes the frequency domain feature extraction process, which effectively reduces the running time by 55.55% and the storage overhead by about 34.6%. The algorithm performance will be further optimized in future research work. Considering that the elimination of voltage transformer design can significantly reduce the cost, the subsequent research will focus on this direction, and is committed to providing more cost-effective solutions for the practical application of NILM, and providing a solid theoretical foundation and feasible technical paths for the design of efficient NILM systems in edge computing environments.
Joint Security-Latency Design for Short Packet-Based Low-Altitude Communications
Authors: Zeyin Wang, Di Zhang, Shaobo Jia, Lulu Song, Yanqun Tang
Date: 2025-04-22 00:00:00

In this article, a joint security and latency analysis of short packet-based low-altitude communications when the eavesdropper is close to the receiver is addressed. To reveal the impacts of the signal-to-noise ratio (SNR) and block-length on latency in communications, we propose a new metric named secure latency (SL) and derive the expressions for the effective secure probability (ESP) and the average SL. To minimize the average SL, different transmission designs are analyzed, in which the optimal solutions of SNR and block-length are provided. Numerical results validate our analysis and reveal the trade-off between reliability and security and the impacts of the block-length, SNR, and packet-generating rate on average SL, of which SNR and the block-length account for main factors. In addition, we find that the performance of SL can be enhanced by allocating less SNR.
Element-Grouping Strategy for Intelligent Reflecting Surface: Performance Analysis and Algorithm Optimization
Authors: Shengsheng Zhang, Taotao Ji, Meng Hua, Yongming Huang, Luxi Yang
Date: 2025-04-22 00:00:00

As a revolutionary paradigm for intelligently controlling wireless channels, intelligent reflecting surface (IRS) has emerged as a promising technology for future sixth-generation (6G) wireless communications. While IRS-aided communication systems can achieve attractive high channel gains, existing schemes require plenty of IRS elements to mitigate the ``multiplicative fading'' effect in cascaded channels, leading to high complexity for real-time beamforming and high signaling overhead for channel estimation. In this paper, the concept of sustainable intelligent element-grouping IRS (IEG-IRS) is proposed to overcome those fundamental bottlenecks. Specifically, based on the statistical channel state information (S-CSI), the proposed grouping strategy intelligently pre-divide the IEG-IRS elements into multiple groups based on the beam-domain grouping method, with each group sharing the common reflection coefficient and being optimized in real time using the instantaneous channel state information (I-CSI). Then, we further analyze the asymptotic performance of the IEG-IRS to reveal the substantial capacity gain in an extremely large-scale IRS (XL-IRS) aided single-user single-input single-output (SU-SISO) system. In particular, when a line-of-sight (LoS) component exists, it demonstrates that the combined cascaded link can be considered as a ``deterministic virtual LoS'' channel, resulting in a sustainable squared array gain achieved by the IEG-IRS. Finally, we formulate a weighted-sum-rate (WSR) maximization problem for an IEG-IRS-aided multiuser multiple-input single-output (MU-MISO) system and a two-stage algorithm for optimizing the beam-domain grouping strategy and the multi-user active-passive beamforming is proposed.