pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing eLoran Timing Accuracy via Machine Learning with Meteorological and Terrain Data
Authors: Taewon Kang, Seunghyeon Park, Pyo-Woong Son, Jiwon Seo
Date: 2025-06-18 00:00:00

The vulnerabilities of global navigation satellite systems (GNSS) to signal interference have increased the demand for complementary positioning, navigation, and timing (PNT) systems. To address this, South Korea has decided to deploy an enhanced long-range navigation (eLoran) system as a complementary PNT solution. Similar to GNSS, eLoran provides highly accurate timing information, which is essential for applications such as telecommunications, financial systems, and power distribution. However, the primary sources of error for GNSS and eLoran differ. For eLoran, the main source of error is signal propagation delay over land, known as the additional secondary factor (ASF). This delay, influenced by ground conductivity and weather conditions along the signal path, is challenging to predict and mitigate. In this paper, we measure the time difference (TD) between GPS and eLoran using a time interval counter and analyze the correlations between eLoran/GPS TD and eleven meteorological factors. Accurate estimation of eLoran/GPS TD could enable eLoran to achieve timing accuracy comparable to that of GPS. We propose two estimation models for eLoran/GPS TD and compare their performance with existing TD estimation methods. The proposed WLR-AGRNN model captures the linear relationships between meteorological factors and eLoran/GPS TD using weighted linear regression (WLR) and models nonlinear relationships between outputs from expert networks through an anisotropic general regression neural network (AGRNN). The model incorporates terrain elevation to appropriately weight meteorological data, as elevation influences signal propagation delay. Experimental results based on four months of data demonstrate that the WLR-AGRNN model outperforms other models, highlighting its effectiveness in improving eLoran/GPS TD estimation accuracy.
Joint Computation Offloading and Resource Allocation for Uncertain Maritime MEC via Cooperation of UAVs and Vessels
Authors: Jiahao You, Ziye Jia, Chao Dong, Qihui Wu, Zhu Han
Date: 2025-06-18 00:00:00
Tasks: Edge-computing

The computation demands from the maritime Internet of Things (MIoT) increase rapidly in recent years, and the unmanned aerial vehicles (UAVs) and vessels based multi-access edge computing (MEC) can fulfill these MIoT requirements. However, the uncertain maritime tasks present significant challenges of inefficient computation offloading and resource allocation. In this paper, we focus on the maritime computation offloading and resource allocation through the cooperation of UAVs and vessels, with consideration of uncertain tasks. Specifically, we propose a cooperative MEC framework for computation offloading and resource allocation, including MIoT devices, UAVs and vessels. Then, we formulate the optimization problem to minimize the total execution time. As for the uncertain MIoT tasks, we leverage Lyapunov optimization to tackle the unpredictable task arrivals and varying computational resource availability. By converting the long-term constraints into short-term constraints, we obtain a set of small-scale optimization problems. Further, considering the heterogeneity of actions and resources of UAVs and vessels, we reformulate the small-scale optimization problem into a Markov game (MG). Moreover, a heterogeneous-agent soft actor-critic is proposed to sequentially update various neural networks and effectively solve the MG problem. Finally, simulations are conducted to verify the effectiveness in addressing computational offloading and resource allocation.
Out-of-Band Modality Synergy Based Multi-User Beam Prediction and Proactive BS Selection with Zero Pilot Overhead
Authors: Kehui Li, Binggui Zhou, Jiajia Guo, Feifei Gao, Guanghua Yang, Shaodan Ma
Date: 2025-06-18 00:00:00
Tasks: Beam Prediction, Management

Multi-user millimeter-wave communication relies on narrow beams and dense cell deployments to ensure reliable connectivity. However, tracking optimal beams for multiple mobile users across multiple base stations (BSs) results in significant signaling overhead. Recent works have explored the capability of out-of-band (OOB) modalities in obtaining spatial characteristics of wireless channels and reducing pilot overhead in single-BS single-user/multi-user systems. However, applying OOB modalities for multi-BS selection towards dense cell deployments leads to high coordination overhead, i.e, excessive computing overhead and high latency in data exchange. How to leverage OOB modalities to eliminate pilot overhead and achieve efficient multi-BS coordination in multi-BS systems remains largely unexplored. In this paper, we propose a novel OOB modality synergy (OMS) based mobility management scheme to realize multi-user beam prediction and proactive BS selection by synergizing two OOB modalities, i.e., vision and location. Specifically, mobile users are initially identified via spatial alignment of visual sensing and location feedback, and then tracked according to the temporal correlation in image sequence. Subsequently, a binary encoding map based gain and beam prediction network (BEM-GBPN) is designed to predict beamforming gains and optimal beams for mobile users at each BS, such that a central unit can control the BSs to perform user handoff and beam switching. Simulation results indicate that the proposed OMS-based mobility management scheme enhances beam prediction and BS selection accuracy and enables users to achieve 91% transmission rates of the optimal with zero pilot overhead and significantly improve multi-BS coordination efficiency compared to existing methods.
Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
Authors: LinLin Wang, Wei Wang, Dezhao Wang, Shanwen Wang
Date: 2025-06-18 00:00:00
Tasks: Deep Learning, Denoising, vehicle detection

With the applicability of optical fiber-based distributed acoustic sensing (DAS) systems, effective signal processing and analysis approaches are needed to promote its popularization in the field of intelligent transportation systems (ITS). This paper presents a signal denoising algorithm using a hybrid deep-learning network (HDLNet). Without annotated data and time-consuming labeling, this self-supervised network runs in parallel, combining an autoencoder for denoising (DAE) and a long short-term memory (LSTM) for sequential processing. Additionally, a line-by-line matching algorithm for vehicle detection and tracking is introduced, thus realizing the complete processing of fiber signal denoising and feature extraction. Experiments were carried out on a self-established real highway tunnel dataset, showing that our proposed hybrid network yields more satisfactory denoising performance than Spatial-domain DAE.
Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks
Authors: Zhihao Tao, Athina P. Petropulu
Date: 2025-06-17 00:00:00

We propose a novel directional modulation (DM) design for OFDM transmitters aided by a time-modulated intelligent reflecting surface (TM-IRS). The TM-IRS is configured to preserve the integrity of transmitted signals toward multiple legitimate users while scrambling the signal in all other directions. Existing TM-IRS design methods typically target a single user direction and follow predefined rule-based procedures, making them unsuitable for multi-user scenarios. Here, we propose a generative AI-based approach to design good sets of TM-IRS parameters out of a set of all possible quantized ranges of parameters. The design objective is to maximize the sum rate across the authorized directions. We model the TM-IRS parameter selection as a deterministic Markov decision process (MDP), where each terminal state corresponds to a specific configuration of TM-IRS parameters. GFlowNets are employed to learn a stochastic policy that samples TM-IRS parameter sets with probability proportional to their associated sum rate reward. Experimental results demonstrate that the proposed method effectively enhances the security of the TM-IRS-aided OFDM systems with multi-users. Also, despite the vast size of the TM-IRS configuration space, the GFlowNet is able to converge after training on fewer than 0.000001% of all possible configurations, demonstrating remarkable efficiency compared to exhaustive combinatorial search. Implementation code is available at https://github.com/ZhihaoTao/GFN4TM-RIS to facilitate reproducibility.
Metasurfaces-Integrated Doubly-Dispersive MIMO: Channel Modeling and Optimization
Authors: Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou, Iván Alexander Morales Sandoval, Giuseppe Thadeu Freitas de Abreu, George C. Alexandropoulos
Date: 2025-06-17 00:00:00
Tasks: ISAC

The doubly-dispersive (DD) channel structure has played a pivotal role in wireless communications, particularly in high-mobility scenarios and integrated sensing and communications (ISAC), due to its ability to capture the key fading effects experienced by a transmitted signal as it propagates through a dynamic medium. However, extending the DD framework to multiple-input multiple-output (MIMO) systems, especially in environments artificially enhanced by reconfigurable intelligent surfaces (RISs) and stacked intelligent metasurfaces (SIM), remains a challenging open problem. In this chapter, a novel metasurfaces-parametrized DD (MPDD) channel model that integrates an arbitrary number of RISs, while also incorporating SIM at both the transmitter and receiver is introduced. Next, the application of this model to some key waveforms optimized for DD environments -- namely orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM) -- is discussed. Finally, the programmability of the proposed model is highlighted through an illustrative application, demonstrating its potential for enhancing waveform performance in SIM-assisted wireless systems.
Integrating Movable Antennas and Intelligent Reflecting Surfaces (MA-IRS): Fundamentals, Practical Solutions, and Opportunities
Authors: Qingqing Wu, Ziyuan Zheng, Ying Gao, Weidong Mei, Xin Wei, Wen Chen, Boyu Ning
Date: 2025-06-17 00:00:00
Tasks: Integrated sensing and communication, Management

Movable antennas (MAs) and intelligent reflecting surfaces (IRSs) enable active antenna repositioning and passive phase-shift tuning for channel reconfiguration, respectively. Integrating MAs and IRSs boosts spatial degrees of freedom, significantly enhancing wireless network capacity, coverage, and reliability. In this article, we first present the fundamentals of MA-IRS integration, involving clarifying the key design issues, revealing performance gain, and identifying the conditions where MA-IRS synergy persists. Then, we examine practical challenges and propose pragmatic design solutions, including optimization schemes, hardware architectures, deployment strategies, and robust designs for hardware impairments and mobility management. In addition, we highlight how MA-IRS architectures uniquely support advanced integrated sensing and communication, enhancing sensing performance and dual-functional flexibility. Overall, MA-IRS integration emerges as a compelling approach toward next-generation reconfigurable wireless systems.
Performance Characterization of Continuous Reconfigurable Intelligent Surfaces
Authors: Amy S. Inwood, Peter J. Smith, Mahmoud AlaaEldin, Michail Matthaiou
Date: 2025-06-17 00:00:00

We consider a reconfigurable intelligent surface (RIS) that can implement a phase rotation continuously over the whole surface rather than via a finite number of discrete elements. Such an RIS can be considered a design for future systems where advances in metamaterials make such an implementation feasible or as the limiting case where the number of elements in a traditional RIS increases in a given area. We derive the optimal RIS design for the single-user (SU) scenario assuming a line-of-sight (LoS) from the RIS to the base station (BS) and correlated Rayleigh fading for the other links. We also derive the associated optimal signal-to-noise ratio (SNR) and its mean, a bound on the mean spectral efficiency (SE), an approximation to the SNR outage probability and an approximation to the coefficient of variation for the investigation of channel hardening.
Lightweight Node Selection in Hexagonal Grid Topology for TDoA-Based UAV Localization
Authors: Zexin Fang, Bin Han, Wenwen Chen, Hans D. Schotten
Date: 2025-06-17 00:00:00

This paper investigates the optimization problem for TDoA-based UAV localization in low-altitude urban environments with hexagonal grid node deployment. We derive a lightweight optimized node selection strategy based on only RSSI measurements, to pre-select optimal nodes, avoiding extensive TDoA measurements in energy-constrained UAV scenarios. Theoretical and simulation results demonstrate that dynamically selecting the number of reference nodes improves localization performance while minimizing resource overhead.
Distributed Activity Detection for Cell-Free Hybrid Near-Far Field Communications
Authors: Jingreng Lei, Yang Li, Zeyi Ren, Qingfeng Lin, Ziyue Wang, Ya-Feng Liu, Yik-Chung Wu
Date: 2025-06-17 00:00:00
Tasks: Action Detection, Activity Detection, CPU

A great amount of endeavor has recently been devoted to activity detection for massive machine-type communications in cell-free massive MIMO. However, in practice, as the number of antennas at the access points (APs) increases, the Rayleigh distance that separates the near-field and far-field regions also expands, rendering the conventional assumption of far-field propagation alone impractical. To address this challenge, this paper considers a hybrid near-far field activity detection in cell-free massive MIMO, and establishes a covariance-based formulation, which facilitates the development of a distributed algorithm to alleviate the computational burden at the central processing unit (CPU). Specifically, each AP performs local activity detection for the devices and then transmits the detection result to the CPU for further processing. In particular, a novel coordinate descent algorithm based on the Sherman-Morrison-Woodbury update with Taylor expansion is proposed to handle the local detection problem at each AP. Moreover, we theoretically analyze how the hybrid near-far field channels affect the detection performance. Simulation results validate the theoretical analysis and demonstrate the superior performance of the proposed approach compared with existing approaches.
A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives
Authors: Zhong Yang, Zhengqiu Zhu, Yong Zhao, Yonglin Tian, Changjun Fan, Runkang Guo, Wenhao Lu, Jingwei Ge, Bin Chen, Yin Zhang, Guohua Wu, Rui Wang, Gyorgy Eigner, Guangquan Cheng, Jincai Huang, Zhong Liu, Jun Zhang, Imre J. Rudas, Fei-Yue Wang
Date: 2025-06-17 00:00:00
Tasks: Federated Learning, reinforcement-learning, Reinforcement Learning, Survey

Underwater target tracking technology plays a pivotal role in marine resource exploration, environmental monitoring, and national defense security. Given that acoustic waves represent an effective medium for long-distance transmission in aquatic environments, underwater acoustic target tracking has become a prominent research area of underwater communications and networking. Existing literature reviews often offer a narrow perspective or inadequately address the paradigm shifts driven by emerging technologies like deep learning and reinforcement learning. To address these gaps, this work presents a systematic survey of this field and introduces an innovative multidimensional taxonomy framework based on target scale, sensor perception modes, and sensor collaboration patterns. Within this framework, we comprehensively survey the literature (more than 180 publications) over the period 2016-2025, spanning from the theoretical foundations to diverse algorithmic approaches in underwater acoustic target tracking. Particularly, we emphasize the transformative potential and recent advancements of machine learning techniques, including deep learning and reinforcement learning, in enhancing the performance and adaptability of underwater tracking systems. Finally, this survey concludes by identifying key challenges in the field and proposing future avenues based on emerging technologies such as federated learning, blockchain, embodied intelligence, and large models.
Dynamically Tunable Helical Antenna
Authors: Ethan Chien, Jan Steckel
Date: 2025-06-16 00:00:00
Tasks: Unity

Unmanned aerial FPV systems demand ultra-low latency, high-reliability communication links. At high speeds and in cluttered environments, Doppler shifts and rapid multipath changes can dramatically raise packet error rates. This paper investigates these phenomena in the context of ExpressLRS (ELRS) long-range FPV control links and demonstrates a novel solution: real-time geometry tuning of a circularly polarized helical antenna array. This study integrates Maxwell-equation-based full-wave simulations (via Ansys HFSS) with controlled, blind field trials to validate performance. A new analysis framework incorporates Doppler-induced frequency offset into the antenna's radiation pattern and the system's error model. Compared to a conventional fixed antenna, the adaptive helical array shows a 20-30% PER reduction when drones exceed 150 mph. The adaptive system automatically adjusts coil pitch and diameter to retune the antenna as flight parameters (velocity, attitude) change. Measured VSWR stays near unity, preventing transmitter reflection spikes. RSSI variation is reduced by half, indicating stronger link stability in urban multi-path. A regression analysis confirms that the reduction in PER due to tuning is highly statistically significant. Calibration data and error analyses are provided to validate our methodology. These findings advance the understanding of high-mobility UAV communication channels and demonstrate that reconfigurable hardware-here, mechanically tunable helices-can effectively counter Doppler and multi-path impairments. The findings inform new design principles for UAV antenna arrays and suggest a path toward AI-integrated adaptive RF systems for drone swarms and racing platforms.
Pinching-Antenna Systems (PASS) Meet Multiple Access: NOMA or OMA?
Authors: Qiao Ren, Xidong Mu, Siyu Lin, Yuanwei Liu
Date: 2025-06-16 00:00:00

A fundamental two-user PASS-based communication system is considered under three MA schemes, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). For each MA scheme, a pinching beamforming optimization problem is formulated to minimize the required transmit power for satisfying users' rate requirements. For NOMA and FDMA, a two-stage algorithm is proposed, where the locations of PAs are derived sequentially by using the successive convex approximation (SCA) method and fine-turning phase adjustment. For TDMA, by leveraging the time-switching feature of PASS, the optimal pinching beamforming of each time slot is derived to maximize the served user channel gain. Numerical results are provided to show that: 1) PASS can achieve a significant performance gain over conventional antenna systems, and 2) NOMA consistently outperforms FDMA, while TDMA provides superior performance than NOMA for symmetric user rate requirements.
Performance Analysis of Communication Signals for Localization in Underwater Sensor Networks
Authors: Ashwani Koul, Gustaf Hendeby, Isaac Skog
Date: 2025-06-16 00:00:00
Tasks: Integrated sensing and communication, ISAC

Fusion of passive and active measurements from sensor nodes becomes critical in localizing underwater objects and is traditionally achieved by communicating information to a central node. This causes significant inefficiencies in bandwidth, energy, and processing time, which are critical in marine applications. With integrated sensing and communication (ISAC) systems, the process of sensing, localization, and communication can be achieved jointly, and the inefficiencies can be minimized. Thus, the primary objective of this study is to analyse the efficacy of such communication signals in localizing a moving target in given underwater conditions. The Cram\'er-Rao Lower Bound (CRLB) is a performance metric used to determine the theoretical lower bound on localization errors. Simulation results illustrate the contours of localization error across various scenarios, offering valuable insights into system performance under different target dynamics and sea state conditions, showcasing their potential for efficient and reliable underwater localization applications.
Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models
Authors: Chuanhong Liu, Caili Guo, Yang Yang, Mingzhe Chen, Tony Q. S. Quek
Date: 2025-06-16 00:00:00
Tasks: Knowledge Distillation, Semantic Communication

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements.
Collaborative Beamforming for Communication Applications Using a Two-Element Fully-Wireless Open-Loop Coherent Distributed Array
Authors: Jason M. Merlo, Jeffrey A. Nanzer
Date: 2025-06-16 00:00:00

In this work we demonstrate a proof of concept of a fully-wireless two-node open-loop coherent distributed communication system and evaluate its performance by transmitting QPSK , 64-, and 256-QAM constellations at a symbol rate of 2 MBd over a 58 m link in an urban environment. The system is implemented in a distributed manner with on-node processing using software-defined radios (SDRs) and wireless internode communication to share coordination information and does not rely on external time or frequency references such as the global navigation satellite system (GNSS). In each experiment ~100 messages were transmitted and a mean coherent gain of 0.936 was achieved across all measurements with a mean symbol error ratio of below $1.4\times 10^{-4}$ achieved up to 64-QAM, demonstrating a reliable bandwidth of up to 12 Mbps.