pwc-archive/papers-with-abstracts huggingface dataset:
Spreading the Wave: Low-Complexity PAPR Reduction for AFDM and OCDM in 6G Networks
Authors: Afan Ali, Abdelali Arous, Huseyin Arslan
Date: 2025-05-03 00:00:00

High Peak-to-Average Power Ratio (PAPR) is still a common issue in multicarrier signal modulation systems such as Orthogonal Chirp Division Multiplexing (OCDM) and Affine Frequency Division Multiplexing (AFDM), which are envisioned to play a central role in 6G networks. To this end, this paper aims to investigate a novel and low-complexity solution towards minimizing the PAPR with the aid of a unified premodulation data spreading paradigm. It analyze four spreading techniques namely, Walsh-Hadamard transform (WHT), Discrete Cosine transform (DCT), Zadoff-Chu transform (ZC), and Interleaved Discrete Fourier transform (IDFT), which assist in preallocating energy prior to OCDM and AFDM modulation. The proposed method takes advantage of the inherent characteristics of chirp-based modulation to achieve a notable reduction in PAPR at minimal computational load and no side information as compared to past solutions, such as Partial Transmit Sequence (PTS) or Selected Mapping (SLM), which suffers with a high computational complexity. The proposed method has an additional benefit of achieving an improvement in phase selectivity by increasing chirp parameters of AFDM and quadratic phase of OCDM, which amplifies the robustness in doubly dispersive channels. It further reduces interference by smoothing the output spread signal. The analytical and simulation results demonstrate an improvement in the overall energy efficiency and scalability of large ioT sensor networks.
Pinching Antenna-enabled ISAC Systems: Exploiting Look-Angle Dependence of RCS for Target Diversity
Authors: Ata Khalili, Brikena Kaziu, Vasilis K. Papanikolaou, Robert Schober
Date: 2025-05-03 00:00:00
Tasks: Diversity, Integrated sensing and communication, ISAC

We investigate a novel integrated sensing and communication (ISAC) system supported by pinching antennas (PAs), which can be dynamically activated along a dielectric waveguide to collect spatially diverse observations. This capability allows different PAs to view the same target from different angles across time, thereby introducing target diversity, which is a key advantage over conventional fixed antenna arrays. To quantify the sensing reliability, we adopt the outage probability as a performance metric, capturing the likelihood that the accumulated radar echo signal power falls below a detection threshold. In contrast to traditional ISAC models that assume deterministic sensing channels, we explicitly account for the look-angle dependence of radar cross-section (RCS) by modeling it as a random variable. We ensure the long-term quality-of-service (QoS) for communication users by enforcing an accumulated data rate constraint over time. We derive an exact closed-form expression for the sensing outage probability based on the distribution of weighted sums of exponentially distributed random variables. Since the resulting expression is highly non-convex and intractable for optimization, we use a tractable upper bound based on the Chernoff inequality and formulate a PA activation optimization problem. A successive convex approximation (SCA) framework is proposed to efficiently solve the formulated problem. Numerical results show that dynamically activating different PAs across time slots significantly enhances sensing reliability compared to repeatedly activating the same PA at a fixed position and conventional antenna selection schemes, respectively. These findings highlight the benefits of integrating outage-based reliability metrics and target diversity into ISAC systems using PAs.
Data-Driven Structural State Estimation via Multi-Fidelity Gaussian Process Models
Authors: Yiming Fan, Fotis Kopsaftopoulos
Date: 2025-05-03 00:00:00
Tasks: State Estimation, Structural Health Monitoring

Guided wave-based techniques have been used extensively in Structural Health Monitoring (SHM). Models using guided waves can provide information from both time and frequency domains to make themselves accurate and robust. Probabilistic SHM models, which have the ability to account for uncertainties, are developed when decision confidence intervals are of interest. Most active-sensing guided-wave methods rely on the assumption that a large dataset can be collected, making them impractical when data collection is constrained by time or environmental factors. Meanwhile, although simulation results may lack the accuracy of real-world data, they are easier to obtain. In this context, models that integrate data from multiple sources have the potential to combine the accuracy of experimental data with the convenience of simulated data, without requiring large and potentially costly experimental datasets. The goal of this work is to introduce and assess a probabilistic multi-fidelity Gaussian process regression framework for damage state estimation via the use of both experimental and simulated guided waves. The main differences from previous works include the integration of damage-sensitive features (damage indices, DIs) extracted from both experimental and numerical sources, as well as the use of a relatively small amount of real-world data. The proposed model was validated by two test cases where multiple data sources exist. For each test case, experimental data were collected from a piezoelectric sensor network attached to an aluminum plate with various structural conditions, while simulated data were generated using either multiphysics finite element model (FEM) or physics-based signal reconstruction approaches under the same conditions.
Understanding the Mechanisms Behind Structural Influences on Link Prediction: A Case Study on FB15k-237
Authors: Xiaobo Jiang, Yadong Deng
Date: 2025-05-03 00:00:00
Tasks: Entity Embeddings, Link Prediction

FB15k-237 mitigates the data leakage issue by excluding inverse and symmetric relationship triples, however, this has led to substantial performance degradation and slow improvement progress. Traditional approaches demonstrate limited effectiveness on FB15k-237, primarily because the underlying mechanism by which structural features of the dataset influence model performance remains unexplored. To bridge this gap, we systematically investigate the impact mechanism of dataset structural features on link prediction performance. Firstly, we design a structured subgraph sampling strategy that ensures connectivity while constructing subgraphs with distinct structural features. Then, through correlation and sensitivity analyses conducted across several mainstream models, we observe that the distribution of relationship categories within subgraphs significantly affects performance, followed by the size of strongly connected components. Further exploration using the LIME model clarifies the intrinsic mechanism by which relationship categories influence link prediction performance, revealing that relationship categories primarily modulate the relative importance between entity embeddings and relationship embeddings and relationship embeddings, thereby affecting link prediction outcomes. These findings provide theoretical insights for addressing performance bottlenecks on FB15k-237, while the proposed analytical framework also offers methodological guidance for future studies dealing with structurally constrained datasets.
Lightweight and Self-Evolving Channel Twinning: An Ensemble DMD-Assisted Approach
Authors: Yashuai Cao, Jintao Wang, Xu Shi, Wei Ni
Date: 2025-04-23 00:00:00

Traditional channel acquisition faces significant limitations due to ideal model assumptions and scalability challenges. A novel environment-aware paradigm, known as channel twinning, tackles these issues by constructing radio propagation environment semantics using a data-driven approach. In the spotlight of channel twinning technology, a radio map is recognized as an effective region-specific model for learning the spatial distribution of channel information. However, most studies focus on static channel map construction, with only a few collecting numerous channel samples and using deep learning for radio map prediction. In this paper, we develop a novel dynamic radio map twinning framework with a substantially small dataset. Specifically, we present an innovative approach that employs dynamic mode decomposition (DMD) to model the evolution of the dynamic channel gain map as a dynamical system. We first interpret dynamic channel gain maps as spatio-temporal video stream data. The coarse-grained and fine-grained evolving modes are extracted from the stream data using a new ensemble DMD (Ens-DMD) algorithm. To mitigate the impact of noisy data, we design a median-based threshold mask technique to filter the noise artifacts of the twin maps. With the proposed DMD-based radio map twinning framework, numerical results are provided to demonstrate the low-complexity reproduction and evolution of the channel gain maps. Furthermore, we consider four radio map twin performance metrics to confirm the superiority of our framework compared to the baselines.
Embracing Diffraction: A Paradigm Shift in Wireless Sensing and Communication
Authors: Anurag Pallaprolu, Winston Hurst, Yasamin Mostofi
Date: 2025-05-02 00:00:00
Tasks: Scene Understanding

Wireless signals are integral to modern society, enabling both communication and increasingly, environmental sensing. While various propagation models exist, ranging from empirical methods to full-wave simulations, the phenomenon of electromagnetic diffraction is often treated as a secondary effect or a correction factor. This paper positions diffraction as a fundamentally important and underutilized mechanism that is rich with information about the physical environment. Specifically, diffraction-inducing elements generate distinct signatures that are rich with information about their underlying properties such as their geometries. We then argue that by understanding and exploiting these relationships, diffraction can be harnessed strategically. We introduce a general optimization framework to formalize this concept, illustrating how diffraction can be leveraged for both inverse problems (sensing scene details such as object geometries from measured fields) and forward problems (shaping RF fields for communication objectives by configuring diffracting elements). Focusing primarily on edge diffraction and Keller's Geometrical Theory of Diffraction (GTD), we discuss specific applications in RF sensing for scene understanding and in communications for RF field programming, drawing upon recent work. Overall, this paper lays out a vision for systematically incorporating diffraction into the design and operation of future wireless systems, paving the way for enhanced sensing capabilities and more robust communication strategies.
Memory-less and Backscatter-less Tunnel Diode Harmonic Signatures for RFID
Authors: Christopher Saetia, Kaitlyn Graves, Serhat Tadik, Gregory D. Durgin
Date: 2025-05-02 00:00:00

Tunnel diodes have traditionally been researched for extending backscatter read-ranges for ultra-high-frequency (UHF) radio-frequency identification (RFID) tags as reflection amplifiers. This paper explores the natural harmonics that arise from biasing these diodes within their negative differential resistance regions and with no interrogating signal from a transmitting source, such as an RFID reader, to injection-lock these diodes. These harmonics are characterized for five tunnel diode boards, made with the same components and with each board's fundamental frequencies measuring at above -15 dBm at a biasing voltage of 200 mV when measured over-the-cable. The occurrence of these harmonics creates unique harmonic signatures for each board and demonstrates possible harmonic RFID applications that can help RFID readers discover and even identify RFID tags with backscatter-less and memory-less IDs generated by tunnel diodes.
Ranging of a Moving Ship Using a Single Acoustic Receiver in Shallow Water
Authors: Junsu Jang, William S Hodgkiss, Florian Meyer
Date: 2025-05-02 00:00:00

Passive acoustics is a versatile tool for maritime situational awareness, enabling applications such as source detection and localization, marine mammal tracking, and geoacoustic inversion. This study focuses on estimating the range between an acoustic receiver and a transiting ship in an acoustically range-independent shallow water environment. Here, acoustic propagation can be modeled by a set of normal modes that are determined by the shallow water waveguide and seabed characteristics. These modes are dispersive, with phase and group velocities varying with frequency, and their interference produces striation patterns that depend on range and frequency in single-hydrophone spectrograms. These patterns can often be characterized by the waveguide invariant, a single parameter describing the waveguide's properties. This paper presents a statistical waveguide invariant-based range estimation approach using a single hydrophone, leveraging broadband and tonal sounds from a transiting ship. Using data from the Seabed Characterization Experiment 2017 (SBCEX17), a large commercial ship's radiated acoustic signature within a 7 Hz bandwidth was processed to estimate its range up to 45 km with errors below three percent.
Super-resolution Wideband Beam Training for Near-field Communications with Ultra-low Overhead
Authors: Cong Zhou, Changsheng You, Shuo Shi, Jiasi Zhou, Chenyu Wu
Date: 2025-04-27 00:00:00
Tasks: Super-Resolution

In this paper, we propose a super-resolution wideband beam training method for near-field communications, which is able to achieve ultra-low overhead. To this end, we first study the multi-beam characteristic of a sparse uniform linear array (S-ULA) in the wideband. Interestingly, we show that this leads to a new beam pattern property, called rainbow blocks, where the S-ULA generates multiple grating lobes and each grating lobe is further splitted into multiple versions in the wideband due to the well-known beam-split effect. As such, one directional beamformer based on S-ULA is capable of generating multiple rainbow blocks in the wideband, hence significantly extending the beam coverage. Then, by exploiting the beam-split effect in both the frequency and spatial domains, we propose a new three-stage wideband beam training method for extremely large-scale array (XL-array) systems. Specifically, we first sparsely activate a set of antennas at the central of the XL-array and judiciously design the time-delay (TD) parameters to estimate candidate user angles by comparing the received signal powers at the user over subcarriers. Next, to resolve the angular ambiguity introduced by the S-ULA, we activate all antennas in the central subarray and design an efficient subcarrier selection scheme to estimate the true user angle. In the third stage, we resolve the user range at the estimated user angle with high resolution by controlling the splitted beams over subcarriers to simultaneously cover the range domain. Finally, numerical results are provided to demonstrate the effectiveness of proposed wideband beam training scheme, which only needs three pilots in near-field beam training, while achieving near-optimal rate performance.
Cramér-Rao Bounds for Integrated Sensing and Communications in Pinching-Antenna Systems
Authors: Dimitrios Bozanis, Vasilis K. Papanikolaou, Sotiris A. Tegos, George K. Karagiannidis
Date: 2025-05-02 00:00:00
Tasks: ISAC

Pinching-antenna systems (PASs) have recently emerged as a flexible, cost-effective route to large-scale antenna deployments envisioned for integrated sensing and communications (ISAC). This paper establishes the fundamental sensing limits of a bistatic PAS link by deriving closed-form Cram\'er-Rao lower bounds for the joint estimation of range and direction when a target is illuminated by pinching antennas placed along a dielectric waveguide and observed by a uniform linear array receiver. By rigorously preserving the amplitude and phase variations of each pinching antenna, as well as exploiting their non-uniform deployment, we gain valuable insights into the performance gain of PASs over conventional antenna arrays. Numerical results validate that the PAS-based ISAC can achieve centimeter-level ranging and sub-degree angular resolution with significantly fewer hardware resources than conventional uniform linear arrays. The derived bounds provide practical design guidelines for next-generation PAS-enabled ISAC systems.
Electronic Nose for Agricultural Grain Pest Detection, Identification, and Monitoring: A Review
Authors: Chetan M Badgujar, Sai Swaminathan, Alison Gerken
Date: 2025-05-02 00:00:00
Tasks: Systematic Literature Review

Biotic pest attacks and infestations are major causes of stored grain losses, leading to significant food and economic losses. Conventional, manual, sampling-based pest recognition methods are labor-intensive, time-consuming, costly, require expertise, and may not even detect hidden infestations. In recent years, the electronic nose (e-nose) approach has emerged as a potential alternative for agricultural grain pest recognition and monitoring. An e-nose mimics human olfactory systems by integrating a sensor array, data acquisition, and analysis for recognizing grain pests by analyzing volatile organic compounds (VOCs) emitted by grain and pests. However, well-documented, curated, and synthesized literature on the use of e-nose technology for grain pest detection is lacking. Therefore, this systematic literature review provides a comprehensive overview of the current state-of-the-art e-nose technology for agricultural grain pest monitoring. The review examines employed sensor technology, targeted pest species type, grain medium, data processing, and pattern recognition techniques. An e-nose is a promising tool that offers a rapid, low-cost, non-destructive solution for detecting, identifying, and monitoring grain pests, including microscopic and hidden insects, with good accuracy. We identified the factors that influence the e-nose performance, which include pest species, storage duration, temperature, moisture content, and pest density. The major challenges include sensor array optimization or selection, large data processing, poor repeatability, and comparability among measurements. An inexpensive and portable e-nose has the potential to help stakeholders and storage managers take timely and data-driven informed actions or decisions to reduce overall food and economic losses.
Network-Level ISAC Design: State-of-the-Art, Challenges, and Opportunities
Authors: Kawon Han, Kaitao Meng, Xiao-Yang Wang, Christos Masouros
Date: 2025-05-02 00:00:00
Tasks: Integrated sensing and communication, ISAC

The ultimate goal of integrated sensing and communication (ISAC) deployment is to provide coordinated sensing and communication services at an unprecedented scale. This paper presents a comprehensive overview of network-level ISAC systems, an emerging paradigm that significantly extends the capabilities of link-level ISAC through distributed cooperation. We first examine recent advancements in network-level ISAC architectures, emphasizing various cooperation schemes and distributed system designs. The sensing and communication (S\&C) performance is analyzed with respect to interference management and cooperative S\&C, offering new insights into the design principles necessary for large-scale networked ISAC deployments. In addition, distributed signaling strategies across different levels of cooperation are reviewed, focusing on key performance metrics such as sensing accuracy and communication quality-of-service (QoS). Next, we explore the key challenges for practical deployment where the critical role of synchronization is also discussed, highlighting advanced over-the-air synchronization techniques specifically tailored for bi-static and distributed ISAC systems. Finally, open challenges and future research directions in network-level ISAC design are identified. The findings and discussions aim to serve as a foundational guideline for advancing scalable, high-performance, and resilient distributed ISAC systems in next-generation wireless networks.
Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance?
Authors: Musa Furkan Keskin, Silvia Mura, Marouan Mizmizi, Dario Tagliaferri, Henk Wymeersch
Date: 2025-05-02 00:00:00
Tasks: ISAC

We investigate data-aided iterative sensing in bistatic OFDM ISAC systems, focusing on scenarios with co-located sensing and communication receivers. To enhance target detection beyond pilot-only sensing methods, we propose a multi-stage bistatic OFDM receiver, performing iterative sensing and data demodulation to progressively refine ISAC channel and data estimates. Simulation results demonstrate that the proposed data-aided scheme significantly outperforms pilot-only benchmarks, particularly in multi-target scenarios, substantially narrowing the performance gap compared to a genie-aided system with perfect data knowledge. Moreover, the proposed approach considerably expands the bistatic ISAC trade-off region, closely approaching the probability of detection-achievable rate boundary established by its genie-aided counterpart.
Simultaneous Multi-Beam Radar with Full Range Resolution exploiting Space-Code Beamforming
Authors: P. Pon Surendar, K. Giridhar
Date: 2025-05-02 00:00:00
Tasks: All

Conventional monostatic radar systems typically exhibit a trade-off between long-range target detection achieved through narrow beams and short-range wide-area surveillance employing broad beams. Realizing both functionalities within a single system, enabling simultaneous surveillance and long-range target localization, poses a significant challenge. This paper presents a novel signal model and an all-digital frequency domain radar architecture leveraging first-of-its-kind space-code beamforming technique to achieve ubiquitous radar coverage. We show that the range-angle map can be estimated for all targets at full range-resolution for all beams compared to existing subcarrier based beamforming radars.
One Target, Many Views: Multi-User Fusion for Collaborative Uplink ISAC
Authors: Sajad Daei, Gabor Fodor, Mikael Skoglund
Date: 2025-05-02 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation, Super-Resolution

We propose a novel pilot-free multi-user uplink framework for integrated sensing and communication (ISAC) in mm-wave networks, where single-antenna users transmit orthogonal frequency division multiplexing signals without dedicated pilots. The base station exploits the spatial and velocity diversities of users to simultaneously decode messages and detect targets, transforming user transmissions into a powerful sensing tool. Each user's signal, structured by a known codebook, propagates through a sparse multi-path channel with shared moving targets and user-specific scatterers. Notably, common targets induce distinct delay-Doppler-angle signatures, while stationary scatterers cluster in parameter space. We formulate the joint multi-path parameter estimation and data decoding as a 3D super-resolution problem, extracting delays, Doppler shifts, and angles-of-arrival via atomic norm minimization, efficiently solved using semidefinite programming. A core innovation is multiuser fusion, where diverse user observations are collaboratively combined to enhance sensing and decoding. This approach improves robustness and integrates multi-user perspectives into a unified estimation framework, enabling high-resolution sensing and reliable communication. Numerical results show that the proposed framework significantly enhances both target estimation and communication performance, highlighting its potential for next-generation ISAC systems.