pwc-archive/papers-with-abstracts huggingface dataset:
One-Bit Sigma-Delta DFRC Waveform Design: Using Quantization Noise for Radar Probing
Authors: Wai-Yiu Keung, Hei Victor Cheng, Wing-Kin Ma
Date: 2025-01-27 00:00:00
Tasks: Quantization

Dual-functional radar-communication (DFRC) signal design has received much attention lately. We consider the scenario of one-bit massive multi-input multi-output (MIMO) wherein one-bit DACs are employed for the sake of saving hardware costs. Specifically, a spatial Sigma-Delta $(\Sigma\Delta)$ modulation scheme is proposed for one-bit MIMO-DFRC waveform design. Unlike the existing approaches which require large-scale binary optimization, the proposed scheme performs $\Sigma\Delta$ modulation on a continuous-valued DFRC signal. The subsequent waveform design is formulated as a constrained least square problem, which can be efficiently solved. Moreover, we leverage quantization noise for radar probing purposes, rather than treating it as unwanted noise. Numerical results demonstrate that the proposed scheme performs well in both radar probing and downlink precoding.
Vehicular Multi-Tier Distributed Computing with Hybrid THz-RF Transmission in Satellite-Terrestrial Integrated Networks
Authors: Ni Zhang, Kunlun Wang, Wen Chen, Jing Xu, Yonghui Li, Arumugam Nallanathan
Date: 2025-01-26 00:00:00
Tasks: Distributed Computing, Edge-computing

In this paper, we propose a Satellite-Terrestrial Integrated Network (STIN) assisted vehicular multi-tier distributed computing (VMDC) system leveraging hybrid terahertz (THz) and radio frequency (RF) communication technologies. Task offloading for satellite edge computing is enabled by THz communication using the orthogonal frequency division multiple access (OFDMA) technique. For terrestrial edge computing, we employ non-orthogonal multiple access (NOMA) and vehicle clustering to realize task offloading. We formulate a non-convex optimization problem aimed at maximizing computation efficiency by jointly optimizing bandwidth allocation, task allocation, subchannel-vehicle matching and power allocation. To address this non-convex optimization problem, we decompose the original problem into four sub-problems and solve them using an alternating iterative optimization approach. For the subproblem of task allocation, we solve it by linear programming. To solve the subproblem of sub-channel allocation, we exploit many-to-one matching theory to obtain the result. The subproblem of bandwidth allocation of OFDMA and the subproblem of power allocation of NOMA are solved by quadratic transformation method. Finally, the simulation results show that our proposed scheme significantly enhances the computation efficiency of the STIN-based VMDC system compared with the benchmark schemes.
Rate-Splitting Sparse Code Multiple Access
Authors: Minerva Priyadarsini, Kuntal Deka, Zilong Liu, Sujit Kumar Sahoo, Sanjeev Sharma
Date: 2025-01-26 00:00:00

This paper presents a novel rate-splitting sparse code multiple access (RS-SCMA) framework, where common messages are transmitted using quadrature phase-shift keying (QPSK) modulation, while private messages are sent using SCMA encoding. A key feature of RS-SCMA is its ability to achieve a tunable overloading factor by adjusting the splitting factor. This flexibility enables an optimal trade-off, ensuring the system maintains superior performance across varying levels of overloading factor. We present a detailed transceiver design and analyze the influence of rate-splitting on the overloading factor. Extensive simulation results, both with and without low-density parity-check (LDPC) codes, highlight RS-SCMA's potential as a strong candidate for next-generation multiple access technologies.
Movable Antenna-Aided Cooperative ISAC Network with Time Synchronization error and Imperfect CSI
Authors: Yue Xiu, Yang Zhao, Ran Yang, Dusit Niyato, Jing Jin, Qixing Wang, Guangyi Liu, Ning Wei
Date: 2025-01-26 00:00:00
Tasks: Deep Reinforcement Learning, Integrated sensing and communication, ISAC

Cooperative-integrated sensing and communication (C-ISAC) networks have emerged as promising solutions for communication and target sensing. However, imperfect channel state information (CSI) estimation and time synchronization (TS) errors degrade performance, affecting communication and sensing accuracy. This paper addresses these challenges {by employing} {movable antennas} (MAs) to enhance C-ISAC robustness. We analyze the impact of CSI errors on achievable rates and introduce a hybrid Cramer-Rao lower bound (HCRLB) to evaluate the effect of TS errors on target localization accuracy. Based on these models, we derive the worst-case achievable rate and sensing precision under such errors. We optimize cooperative beamforming, {base station (BS)} selection factor and MA position to minimize power consumption while ensuring accuracy. {We then propose a} constrained deep reinforcement learning (C-DRL) approach to solve this non-convex optimization problem, using a modified deep deterministic policy gradient (DDPG) algorithm with a Wolpertinger architecture for efficient training under complex constraints. {Simulation results show that the proposed method significantly improves system robustness against CSI and TS errors, where robustness mean reliable data transmission under poor channel conditions.} These findings demonstrate the potential of MA technology to reduce power consumption in imperfect CSI and TS environments.
Modern Base Station Architecture: Enabling Passive Beamforming with Beyond Diagonal RISs
Authors: Mahmoud Raeisi, Hui Chen, Henk Wymeersch, Ertugrul Basar
Date: 2025-01-26 00:00:00

Beamforming plays a crucial role in millimeter wave (mmWave) communication systems to mitigate the severe attenuation inherent to this spectrum. However, the use of large active antenna arrays in conventional architectures often results in high implementation costs and excessive power consumption, limiting their practicality. As an alternative, deploying large arrays at transceivers using passive devices, such as reconfigurable intelligent surfaces (RISs), offers a more cost-effective and energy-efficient solution. In this paper, we investigate a promising base station (BS) architecture that integrates a beyond diagonal RIS (BD-RIS) within the BS to enable passive beamforming. By utilizing Takagi's decomposition and leveraging the effective beamforming vector, the RIS profile can be designed to enable passive beamforming directed toward the target. Through the beamforming analysis, we reveal that BD-RIS provides robust beamforming performance across various system configurations, whereas the traditional diagonal RIS (D-RIS) exhibits instability with increasing RIS size and decreasing BS-RIS separation-two critical factors in optimizing RIS-assisted systems. Comprehensive computer simulation results across various aspects validate the superiority of the proposed BS-integrated BD-RIS over conventional D-RIS architectures, showcasing performance comparable to active analog beamforming antenna arrays.
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study
Authors: Mengxi Liu, Daniel Geißler, Sizhen Bian, Bo Zhou, Paul Lukowicz
Date: 2025-01-25 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Missing Values, Time Series

Human activity recognition (HAR) ideally relies on data from wearable or environment-instrumented sensors sampled at regular intervals, enabling standard neural network models optimized for consistent time-series data as input. However, real-world sensor data often exhibits irregular sampling due to, for example, hardware constraints, power-saving measures, or communication delays, posing challenges for deployed static HAR models. This study assesses the impact of sampling irregularities on HAR by simulating irregular data through two methods: introducing slight inconsistencies in sampling intervals (timestamp variations) to mimic sensor jitter, and randomly removing data points (random dropout) to simulate missing values due to packet loss or sensor failure. We evaluate both discrete-time neural networks and continuous-time neural networks, which are designed to handle continuous-time data, on three public datasets. We demonstrate that timestamp variations do not significantly affect the performance of discrete-time neural networks, and the continuous-time neural network is also ineffective in addressing the challenges posed by irregular sampling, possibly due to limitations in modeling complex temporal patterns with missing data. Our findings underscore the necessity for new models or approaches that can robustly handle sampling irregularity in time-series data, like the reading in human activity recognition, paving the way for future research in this domain.
Kalman filter/deep-learning hybrid automatic boundary tracking of optical coherence tomography data for deep anterior lamellar keratoplasty (DALK)
Authors: Hongrui Yi, Jinglun Yu, Yaning Wang, Justin Opfermann, Bill G. Gensheimer, Axel Kriger, Jin U. Kang
Date: 2025-01-25 00:00:00
Tasks: Segmentation

Deep anterior lamellar keratoplasty (DALK) is a highly challenging partial thickness cornea transplant surgery that replaces the anterior cornea above Descemet's membrane (DM) with a donor cornea. In our previous work, we proposed the design of an optical coherence tomography (OCT) sensor integrated needle to acquire real-time M-mode images to provide depth feedback during OCT-guided needle insertion during Big Bubble DALK procedures. Machine learning and deep learning techniques were applied to M-mode images to automatically identify the DM in OCT M-scan data. However, such segmentation methods often produce inconsistent or jagged segmentation of the DM which reduces the model accuracy. Here we present a Kalman filter based OCT M-scan boundary tracking algorithm in addition to AI-based precise needle guidance to improve automatic DM segmentation for OCT-guided DALK procedures. By using the Kalman filter, the proposed method generates a smoother layer segmentation result from OCT M-mode images for more accurate tracking of the DM layer and epithelium. Initial ex vivo testing demonstrates that the proposed approach significantly increases the segmentation accuracy compared to conventional methods without the Kalman filter. Our proposed model can provide more consistent and precise depth sensing results, which has great potential to improve surgical safety and ultimately contributes to better patient outcomes.
Fusion of Millimeter-wave Radar and Pulse Oximeter Data for Low-burden Diagnosis of Obstructive Sleep Apnea-Hypopnea Syndrome
Authors: Wei Wang, Zhaoxi Chen, Wenyu Zhang, Zetao Wang, Xiang Zhao, Chenyang Li, Jian Guan, Shankai Yin, Gang Li
Date: 2025-01-25 00:00:00
Tasks: Diagnostic, Sleep Staging, Temporal Localization

Objective: The aim of the study is to develop a novel method for improved diagnosis of obstructive sleep apnea-hypopnea syndrome (OSAHS) in clinical or home settings, with the focus on achieving diagnostic performance comparable to the gold-standard polysomnography (PSG) with significantly reduced monitoring burden. Methods: We propose a method using millimeter-wave radar and pulse oximeter for OSAHS diagnosis (ROSA). It contains a sleep apnea-hypopnea events (SAE) detection network, which directly predicts the temporal localization of SAE, and a sleep staging network, which predicts the sleep stages throughout the night, based on radar signals. It also fuses oxygen saturation (SpO2) information from the pulse oximeter to adjust the score of SAE detected by radar. Results: Experimental results on a real-world dataset (>800 hours of overnight recordings, 100 subjects) demonstrated high agreement (ICC=0.9870) on apnea-hypopnea index (AHI) between ROSA and PSG. ROSA also exhibited excellent diagnostic performance, exceeding 90% in accuracy across AHI diagnostic thresholds of 5, 15 and 30 events/h. Conclusion: ROSA improves diagnostic accuracy by fusing millimeter-wave radar and pulse oximeter data. It provides a reliable and low-burden solution for OSAHS diagnosis. Significance: ROSA addresses the limitations of high complexity and monitoring burden associated with traditional PSG. The high accuracy and low burden of ROSA show its potential to improve the accessibility of OSAHS diagnosis among population.
Deep Multimodal Learning for Real-Time DDoS Attacks Detection in Internet of Vehicles
Authors: Mohamed Ababsa, Soheyb Ribouh, Abdelhamid Malki, Lyes Khoukhi
Date: 2025-01-25 00:00:00

The progress and integration of intelligent transport systems (ITS) have therefore been central to creating safer and more efficient transport networks. The Internet of Vehicles (IoV) has the potential to improve road safety and provide comfort to travelers. However, this technology is exposed to a variety of security vulnerabilities that malicious actors could exploit. One of the most serious threats to IoV is the Distributed Denial of Service (DDoS) attack, which could be used to disrupt traffic flow, disable communication between vehicles, or even cause accidents. In this paper, we propose a novel Deep Multimodal Learning (DML) approach for detecting DDoS attacks in IoV, addressing a critical aspect of cybersecurity in intelligent transport systems. Our proposed DML model integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), enhanced by Attention and Gating mechanisms, and Multi-Layer Perceptron (MLP) with a multimodal intermediate fusion architecture. This innovative method effectively identifies and mitigates DDoS attacks in real-time by utilizing the Framework for Misbehavior Detection (F2MD) to generate a synthetic dataset, thereby overcoming the limitations of the existing Vehicular Reference Misbehavior (VeReMi) extension dataset. The proposed approach is evaluated in real-time across different simulated real-world scenario with 10\%, $30\%$, and $50\%$ attacker densities. The proposed DML model achieves an average accuracy of 96.63\%, outperforming the classical Machine Learning (ML) approaches and state-of-the-art methods which demonstrate significant efficacy and reliability in protecting vehicular networks from malicious cyber-attacks.
A parametric non-negative coupled canonical polyadic decomposition algorithm for hyperspectral super-resolution
Authors: Xi-Yuan Liu, Xiao-Feng Gong, Lei Wang, Wei Feng, Qiu-Hua Lin
Date: 2025-01-25 00:00:00
Tasks: Super-Resolution, Tensor Decomposition

Recently, coupled tensor decomposition has been widely used in data fusion of a hyperspectral image (HSI) and a multispectral image (MSI) for hyperspectral super-resolution (HSR). However, exsiting works often ignore the inherent non-negative (NN) property of the image data, or impose the NN constraint via hard-thresholding which may interfere with the optimization procedure and cause the method to be sub-optimal. As such, we propose a novel NN coupled canonical polyadic decomposition (NN-C-CPD) algorithm, which makes use of the parametric method and nonlinear least squares (NLS) framework to impose the NN constraint into the C-CPD computation. More exactly, the NN constraint is converted into the squared relationship between the NN entries of the factor matrices and a set of latent parameters. Based on the chain rule for deriving the derivatives, the key entities such as gradient and Jacobian with regards to the latent parameters can be derived, thus the NN constraint is naturally integrated without interfering with the optimization procedure. Experimental results are provided to demonstrate the performance of the proposed NN-C-CPD algorithm in HSR applications.
Flexible Cylindrical Arrays with Movable Antennas for MISO System: Beamforming and Position Optimization
Authors: Jiahe Guo, Songjie Yang, Xiangyu Dong, Jiapan Yang, Junfeng Deng, Zhongpei Zhang, Chau Yuen
Date: 2025-01-25 00:00:00
Tasks: Position

As wireless communication advances toward the 6G era, the demand for ultra-reliable, high-speed, and ubiquitous connectivity is driving the exploration of new degrees-of-freedom (DoFs) in communication systems. Among the key enabling technologies, Movable Antennas (MAs) integrated into Flexible Cylindrical Arrays (FCLA) have shown great potential in optimizing wireless communication by providing spatial flexibility. This paper proposes an innovative optimization framework that leverages the dynamic mobility of FCLAs to improve communication rates and overall system performance. By employing Fractional Programming (FP) for alternating optimization of beamforming and antenna positions, the system enhances throughput and resource utilization. Additionally, a novel Constrained Grid Search-Based Adaptive Moment Estimation Algorithm (CGS-Adam) is introduced to optimize antenna positions while adhering to antenna spacing constraints. Extensive simulations validate that the proposed system, utilizing movable antennas, significantly outperforms traditional fixed antenna optimization, achieving up to a 31\% performance gain in general scenarios. The integration of FCLAs in wireless networks represents a promising solution for future 6G systems, offering improved coverage, energy efficiency, and flexibility.
Performance evaluation of non-uniform sensor spacing in a linear array configuration for MUSIC algorithm
Authors: Pradeep Dheerendra, Sumit Saraogi, Palanisamy P., Kalyanasundaram N
Date: 2025-01-25 00:00:00

In this paper, the performance of non-uniform spacing of sensors is evaluated for the MUSIC algorithm which estimates the direction of arrival (DOA) of a narrowband plane wave impinging on an array of sensors. Unlike uniform sensor spacing arrangement, where sensors are equidistant (equal to half the wavelength), we consider non-uniform spacing for the arrangement of sensors, where the distance between consecutive sensors increases progressively. We observe that the non-uniform sensor spacing configuration (with lesser number of sensors) can provide similar or better accuracy in DOA estimation compared to uniform sensor spacing configuration despite more number of sensors at identical array length.
Three-Dimensional Sparse Random Mode Decomposition for Mode Disentangling with Crossover Instantaneous Frequencies
Authors: Chen Luo, Tao Chen, Lei Xie, Hongye Su
Date: 2025-01-25 00:00:00

Sparse random mode decomposition (SRMD) is a novel algorithm that constructs a random time-frequency feature space to sparsely approximate spectrograms, effectively separating modes. However, it fails to distinguish adjacent or overlapped frequency components, especially, those with crossover instantaneous frequencies. To address this limitation, an enhanced version, termed three-dimensional SRMD (3D-SRMD), is proposed in this letter. In 3D-SRMD, the random features are lifted from a two-dimensional space to a three-dimensional (3D) space by introducing one extra chirp rate axis. This enhancement effectively disentangles the frequency components overlapped in the low dimension. Additionally, a novel random feature generation strategy is designed to improve the separation accuracy of 3D-SRMD by combining the 3D ridge detection method. Finally, numerical experiments on both simulated and real-world signals demonstrate the effectiveness of our method.
A Block Term Decomposition Model Based Algorithm for Tensor Completion of Multidimensional Harmonic Signals
Authors: Lei Wang, Xiao-Feng Gong, Xi-Yuan Liu, Wei Feng, Qiu-Hua Lin
Date: 2025-01-25 00:00:00

We consider tensor data completion of an incomplete observation of multidimensional harmonic (MH) signals. Unlike existing tensor-based techniques for MH retrieval (MHR), which mostly adopt the canonical polyadic decomposition (CPD) to model the simple "one-to-one" correspondence among harmonics across difference modes, we herein use the more flexible block term decomposition (BTD) model that can be used to describe the complex mutual correspondences among several groups of harmonics across different modes. An optimization principle that aims to fit the BTD model in the least squares sense, subject to rank minimization of hankelized MH components, is set up for the tensor completion task, and an algorithm based on alternating direction method of multipliers is proposed, of which the effectiveness and applicability are validated through both numerical simulations and an application in Sub-6GHz channel state information (CSI) completion.
Target Localization with a Coprime Multistatic MIMO Radar via Coupled Canonical Polyadic Decomposition Based on Joint EVD
Authors: Guo-Zhao Liao, Xiao-Feng Gong, Wei Liu, Hing Cheung So
Date: 2025-01-25 00:00:00

This paper addresses target localization using a multistatic multiple-input multiple-output (MIMO) radar system with coprime L-shaped receive arrays (CLsA). A target localization method is proposed by modeling the observed signals as tensors that admit a coupled canonical polyadic decomposition (C-CPD) model without matched filtering. It consists of a novel joint eigenvalue decomposition (J-EVD) based (semi-)algebraic algorithm, and a post-processing approach to determine the target locations by fusing the direction-of-arrival estimates extracted from J-EVD-based CCPD results. Particularly, by leveraging the rotational invariance of Vandermonde structure in CLsA, we convert the CCPD problem into a J-EVD problem, significantly reducing its computational complexity. Experimental results show that our method outperforms existing tensor-based ones.
Crystal Oscillators in OSNMA-Enabled Receivers: An Implementation View for Automotive Applications
Authors: Francesco Ardizzon, Nicola Laurenti, Carlo Sarto, Giovanni Gamba, Cillian O'Driscoll, Ignacio Fernandez-Hernandez
Date: 2025-01-25 00:00:00

To ensure the authenticity of navigation data, Galileo Open Service navigation message authentication (OSNMA) requires loose synchronization between the receiver clock and the system time. This means that during the period between clock calibrations, the receiver clock error needs to be smaller than a pre-defined threshold, currently up to 165s for OSNMA. On the other hand, relying on the PVT solution to steer the receiver clock or correct its bias may not be possible since this would depend on the very same signals we intend to authenticate. This work aims to investigate the causes of the frequency accuracy loss leading to clock errors and to build a model that, from the datasheet of a real-time clock (RTC) device, allows to bound the error clock during a certain period. The model's main contributors are temperature changes, long-term aging, and offset at calibration, but it includes other factors. We then apply the model to several RTCs from different manufacturers and bound the maximum error for certain periods, with a focus on the two-year between-calibration period expected for the smart tachograph, an automotive application that will integrate OSNMA.