pwc-archive/papers-with-abstracts huggingface dataset:
A Two-Stage Rotation-Based Super-Resolution Signature Estimation for Spatial Wideband Systems
Authors: Chandrashekhar Rai, Debarati Sen
Date: 2025-03-23 00:00:00
Tasks: Computational Efficiency, Super-Resolution

Spatial and temporal delays in a wireless multi-antenna system, paired with an orthogonal frequency division multiplexing (OFDM) waveform, can be utilized to estimate the Angle of Arrival (AoA) and Time of Arrival (ToA) of scatterers in the radio channel through spectral estimation techniques. However, in millimeter-wave (mmWave) and TeraHertz (THz) systems, the spatial delays across the aperture of massive array elements are comparable to the inverse of the signal bandwidth. As a result, these delays cannot be approximated solely by phase terms, necessitating consideration of the Spatial Wideband Effect (SWE). The SWE in the mmWave/THz system causes migration of the actual AoA-ToA coarse bins. Moreover, a finite grid measurement of complex sinusoidal signals of continuous frequencies results in spectral leakage whenever there is a grid mismatch. In this work, given the Discrete Fourier Transform's computational efficiency and broad practical applicability, we propose utilizing the inverse Discrete Fourier Transform (DFT) for the initial 2-D spectrum estimation of the channel response. Further, in this paper, we propose a two-stage efficient rotation-based algorithm for fine-tuned signature estimation of spatial wideband systems with uniform linear arrays. Specifically, we utilize the rotation-based method to identify the correct coarse bin in the first stage followed by 2D-rotation based fine-tuning around the corrected coarse bin in the second stage. The proposed technique in this work can be used for handling beam squint effect in different applications like near-filed communications, wideband Multiple Input Multiple Output (MIMO) radar, channel estimation in Extremely Large (XL)-MIMO for 6G and beyond systems etc. The effectiveness of our proposed algorithm over the existing narrowband super-resolution estimation algorithms is established numerically through computer simulations.
GenMetaLoc: Learning to Learn Environment-Aware Fingerprint Generation for Sample Efficient Wireless Localization
Authors: Jun Gao, Feng Yin, Wenzhong Yan, Qinglei Kong, Lexi Xu, Shuguang Cui
Date: 2025-03-23 00:00:00
Tasks: Meta-Learning

Existing fingerprinting-based localization methods often require extensive data collection and struggle to generalize to new environments. In contrast to previous environment-unknown MetaLoc, we propose GenMetaLoc in this paper, which first introduces meta-learning to enable the generation of dense fingerprint databases from an environment-aware perspective. In the model aspect, the learning-to-learn mechanism accelerates the fingerprint generation process by facilitating rapid adaptation to new environments with minimal data. Additionally, we incorporate 3D point cloud data from the first Fresnel zone between the transmitter and receiver, which describes the obstacles distribution in the environment and serves as a condition to guide the diffusion model in generating more accurate fingerprints. In the data processing aspect, unlike most studies that focus solely on channel state information (CSI) amplitude or phase, we present a comprehensive processing that addresses both, correcting errors from WiFi hardware limitations such as amplitude discrepancies and frequency offsets. For the data collection platform, we develop an uplink wireless localization system that leverages the sensing capabilities of existing commercial WiFi devices and mobile phones, thus reducing the need for additional deployment costs. Experimental results on real datasets show that our framework outperforms baseline methods.
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes
Authors: Tao Fang, Majid Zamani
Date: 2025-03-23 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, Spike Sorting

There is a need for fast adaptation in spike sorting algorithms to implement brain-machine interface (BMIs) in different applications. Learning and adapting the functionality of the sorting process in real-time can significantly improve the performance. However, deep neural networks (DNNs) depend on large amounts of data for training models and their performance sustainability decreases when data is limited. Inspired by meta-learning, this paper proposes a few-shot spike sorting framework (FS-SS) with variable network model size that requires minimal learning training and supervision. The framework is not only compatible with few-shot adaptations, but also it uses attention mechanism and dilated convolutional neural networks. This allows scaling the network parameters to learn the important features of spike signals and to quickly generalize the learning ability to new spike waveforms in recording channels after few observations. The FS-SS was evaluated by using freely accessible datasets, also compared with the other state-of-the-art algorithms. The average classification accuracy of the proposed method is 99.28%, which shows extreme robustness to background noise and similarity of the spike waveforms. When the number of training samples is reduced by 90%, the parameter scale is reduced by 68.2%, while the accuracy only decreased by 0.55%. The paper also visualizes the model's attention distribution under spike sorting tasks of different difficulty levels. The attention distribution results show that the proposed model has clear interpretability and high robustness.
Rejuvenating IRS: AoI-based Low Overhead Reconfiguration Design
Authors: Jorge Torres Gómez, Joana Angjo, Moritz Garkisch, Vahid Jamali, Robert Schober, Falko Dressler
Date: 2025-03-23 00:00:00

Intelligent reflective surface (IRS) technologies help mitigate undesirable effects in wireless links by steering the communication signal between transmitters and receivers. IRS elements are configured to adjust the phase of the reflected signal for a user's location and enhance the perceived signal-to-noise ratio (SNR). In this way, an IRS improves the communication link but inevitably introduces more communication overhead. This occurs especially in mobile scenarios, where the user's position must be frequently estimated to re-adjust the IRS elements periodically. Such an operation requires balancing the amount of training versus the data time slots to optimize the communication performance in the link. Aiming to study this balance with the age of information (AoI) framework, we address the question of how often an IRS needs to be updated with the lowest possible overhead and the maximum of freshness of information. We derive the corresponding analytical solution for a mobile scenario, where the transmitter is static and the mobile user (MU) follows a random waypoint mobility model. We provide a closed-form expression for the average peak age of information (PAoI), as a metric to evaluate the impact of the IRS update frequency. As for the performance evaluation, we consider a realistic scenario following the IEEE 802.11ad standard, targeting the mmWave band. Our results reveal that the minimum achievable average PAoI is in the microsecond range and the optimal IRS update period is in the seconds range, causing 9% overhead in the link when the MU moves at a velocity of 1 m/s.
Opportunistic Subarray Grouping for RIS-Aided Massive Random Access in Cellular Connectivity
Authors: Yizhu Wang, Zhou Zhang, Saman Atapattu, Marco Di Renzo
Date: 2025-03-23 00:00:00
Tasks: Scheduling

In Reconfigurable Intelligent Surfaces (RIS), reflective elements (REs) are typically configured as a single array, but as RE numbers increase, this approach incurs high overhead for optimal configuration. Subarray grouping provides an effective tradeoff between performance and overhead. This paper studies RIS-aided massive random access (RA) at the Medium Access Control (MAC) layer in cellular networks to enhance throughput. We introduce an opportunistic scheduling scheme that integrates multi-round access requests, subarray grouping for efficient RIS link acquisition, and multi-user data transmission. To optimize access request timing, RIS estimation overhead and throughput, we propose a multi-user RA strategy using sequential decision optimization to maximize average system throughput. A low-complexity algorithm is also developed for practical implementation. Both theoretical analysis and numerical simulations demonstrate that the proposed strategy significantly outperforms the extremes of full-array grouping and element-wise grouping.
Geometry-Based Channel Estimation, Prediction, and Fusion
Authors: Benjamin J. B. Deutschmann, Erik Leitinger, Klaus Witrisal
Date: 2025-03-22 00:00:00
Tasks: Position, Prediction

Reciprocity-based beamforming-most commonly employed in time-division duplexing-uses noisy, estimated (i.e., measured) channel state information (CSI) acquired on the uplink. While computationally efficient, reciprocity-based beamforming suffers severe losses under (i) low signal-to-noise ratio (SNR) and (ii) user mobility because it ignores the underlying physics of the radio channel beyond its reciprocity. Based on a physics-driven geometry-based channel model, we propose a method that jointly infers the mobile user's position and environment map on the uplink. It then leverages the estimated user position and environment map to predict CSI on the downlink. We demonstrate significant efficiency gains under both (i) low SNR and (ii) user mobility on measured data. While the user position may allow efficient beamforming in strong line-of-sight (LoS) channels, inferring an environment map allows bypassing obstructed LoS conditions using non-LoS beamforming via multipath components. We further propose "channel fusion," a probabilistic (Bayesian) combination of estimated and predicted CSI, which increases the beamforming robustness, particularly when either source of CSI is unreliable. Notably, this approach shares similarities with the minimum mean square error (MMSE) channel estimator, with geometry-based prior parameters inferred from the data.
Green Integration of Sensing, Communication, and Power Transfer via STAR-RIS
Authors: Vaibhav Kumar, Marwa Chafii
Date: 2025-03-22 00:00:00

The upcoming sixth-generation (6G) wireless standard is anticipated to support a variety of applications that will require a seamless integration of sensing and communication services in a single network infrastructure. At the same time, 6G is also expected to support millions of low-powered Internet-of-Things (IoT) devices. Previous studies have demonstrated that the power requirements of these IoT devices can be met through wireless power transfer facilitated by intelligent metasurfaces. Therefore, in this paper, we try to formulate and answer a fundamental question: How much transmit power is required for an integrated sensing, communication, and power transfer (ISCPT) system? More specifically, we consider the problem of optimal active, passive, and receive beamforming design for a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled ISCPT system with multiple sensing targets, multiple information receivers, and multiple energy receivers, to minimize the required transmit power from the base station, while guaranteeing predefined sensing, communication, and energy harvesting requirements. To tackle the challenging non-convex optimization problem, we use an alternating optimization (AO)-based approach, where the receive beamforming is obtained in closed form while the active and passive beamforming are obtained using a second-order cone program (SOCP) approach. Our numerical results show the benefit of using STAR-RIS to reduce the transmit power requirement for the ISCPT system compared to its corresponding conventional RIS (cRIS)-enabled and non-RIS ISCPT systems.
Achievable Sum Secrecy Rate of STAR-RIS-Enabled MU-MIMO ISAC
Authors: Ainara Kazymova, Vaibhav Kumar, Christina Pöpper, Marwa Chafii
Date: 2025-03-22 00:00:00
Tasks: Integrated sensing and communication, ISAC

Various emerging applications in sixth-generation (6G) wireless demand a seamless integration of communication and sensing services, driving the development of integrated sensing and communication (ISAC) systems. Using a common waveform for both functions introduces additional security challenges, as information-bearing signals are vulnerable to eavesdropping. While a variety of secure beamforming designs are proposed in the literature for metasurface-enabled ISAC systems with single-antenna users and eavesdroppers, optimal designs for multi-antenna scenarios remain unexplored. This paper addresses this gap by studying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled multi-user multiple-input multiple-output (MU-MIMO) ISAC system and proposing an optimal beamforming design to maximize the sum secrecy rate while ensuring sensing quality for multiple targets. An alternating-optimization based iterative solution is developed to tackle the non-convex optimization problem. The presented analysis confirms that the computational complexity of the proposed algorithm grows linearly with the number of metasurface elements. Our numerical results show the benefit of using STAR-RIS to increase the sum secrecy rate of the MU-MIMO ISAC system compared to its corresponding conventional RIS (cRIS)-enabled and w/o RIS systems.
Single-Satellite-Based Geolocation of Broadcast GNSS Spoofers from Low Earth Orbit
Authors: Zachary L. Clements, Patrick B. Ellis, Iain Goodridge, Matthew J. Murrian, Mark L. Psiaki, Todd E. Humphreys
Date: 2025-03-22 00:00:00

This paper presents an analysis and experimental demonstration of single-satellite single-pass geolocation of a terrestrial broadcast Global Navigation Satellite System (GNSS) spoofer from Low Earth Orbit (LEO). The proliferation of LEO-based GNSS receivers offers the prospect of unprecedented spectrum awareness, enabling persistent GNSS interference detection and geolocation. Accurate LEO-based single-receiver emitter geolocation is possible when a range-rate time history can be extracted for the emitter. This paper presents a technique crafted specifically for indiscriminate broadcast-type GNSS spoofing signals. Furthermore, it explores how unmodeled oscillator instability and worst-case spoofer-introduced signal variations degrade the geolocation estimate. The proposed geolocation technique is validated by a controlled experiment, in partnership with Spire Global, in which a LEO-based receiver captures broadcast GNSS spoofing signals transmitted from a known ground station on a non-GNSS frequency band.
A Novel Design Method for Seeking Sparse Linear Arrays With Low Redundancy and Enhanced DOF
Authors: Si Wang, GuoQiang Xiao
Date: 2025-03-22 00:00:00

Sparse arrays with $N$-sensors can provide up to $O(N^2)$ degrees of freedom (DOF) by second-order cumulants. However, these sparse arrays like minimum-/low-redundancy arrays (MRAs/LRAs), nested arrays and coprime arrays can only provide limited DOF and array aperture with the same number of physical sensors. However, further increasing DOF would increase costs in practical applications. The paper aims to design a sparse linear array (SLA) with higher DOF and lower redundancy via exploring different cases of third-order cumulants. Based on the framework third-order exhaustive co-array (TO-ECA), a general third-order array (GTOA) with any generator is proposed in the paper. Further, three novel arrays are designed based on GTOA with different generators, namely third-order sum and difference array (generator) (TO-SDA(CNA)), (TO-SDA(SCNA)) and (TO-SDA(TNA-II)) which can provide closed-form expressions for the sensor locations and enhance DOF in order to resolve more signal sources in the estimation of direction of arrival (DOA). The three TO-SDAs are all consisted of two sub-arrays, where the first is the generator and another is a ULA with big inter-spacing between sensors. For the three TO-SDAs, the maximum DOF under the given number of total physical sensors can be derived and the TO-ECA of the three TO-SDAs are hole-free. Additionally, the redundancy of the three TO-SDAs is defined, and the lower band of the redundancy for the three TO-SDAs is derived. Furthermore, the proposed TO-SDA(TNA-II) not only achieves higher DOF than those of existing TONA and even SE-FL-NA but also reduces mutual coupling effects. Meanwhile it realizes higher resolution and decreases redundancy. Numerical simulations are conducted to verify the superiority of TO-SDA(TNA-II) on DOA estimation performance and enhanced DOF over other existing DCAs.
Investigation into the role of the Bessel function order in the Fourier-Bessel series and the Hankel Transform
Authors: Suketu P Patni, Vikram M Gadre
Date: 2025-03-22 00:00:00

This work focuses on estimating the number of terms of a Fourier-Bessel series of order $p'$ required to get within a certain error of a Bessel function of a fixed order $p$ where $p \neq p'$. Our approach consists of two steps: one, constructing an invariant over $n$ of the $n^{\text{th}}$ order Hankel transform; and two, observing the effect of expanding a suitably scaled Bessel function of a fixed order $p$ in its Fourier-Bessel series of order $p'$. We demonstrate a new error metric to simplify the error computations. Further, we generate an empirical model using numerical simulations and examine its capabilities in predicting the number of terms required.
RIS-based Physical Layer Security for Integrated Sensing and Communication: A Comprehensive Survey
Authors: Yongxiao Li, Feroz Khan, Manzoor Ahmed, Aized Amin Soofi, Wali Ullah Khan, Chandan Kumar Sheemar, Muhammad Asif, Zhu Han
Date: 2025-03-22 00:00:00
Tasks: Integrated sensing and communication, ISAC, Survey

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C\&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C\&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C\&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into passive RIS and Active RIS (ARIS) paradigms. Passive RIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.
Flexible WMMSE Beamforming for MU-MIMO Movable Antenna Communications
Authors: Songjie Yang, Zihang Wan, Yue Xiu, Boyu Ning, Yong Li, Yuenwei Liu, Chau Yuen
Date: 2025-03-22 00:00:00
Tasks: LEMMA, Position

Movable antennas offer new potential for wireless communication by introducing degrees of freedom in antenna positioning, which has recently been explored for improving sum rates. In this paper, we aim to fully leverage the capabilities of movable antennas (MAs) by assuming that both the transmitter and receiver can optimize their antenna positions in multi-user multiple-input multiple-output (MU-MIMO) communications. Recognizing that WMMSE beamforming is a highly effective method for maximizing the MU-MIMO sum rate, we modify it to integrate antenna position optimization for MA systems, which we refer to as flexible WMMSE (F-WMMSE) beamforming. Importantly, we reformulate the subproblems within WMMSE to develop regularized sparse optimization frameworks to achieve joint beamforming (antenna coefficient optimization) and element movement (antenna position optimization). We then propose a regularized least squares-based simultaneous orthogonal matching pursuit (RLS-SOMP) algorithm to address the resulting sparse optimization problem. To enhance practical applications, the low-complexity implementation of the proposed framework is developed based on the pre-calculations and matrix inverse lemma. The overall F-WMMSE algorithm converges similarly to WMMSE, and our findings indicate that F-WMMSE achieves a significant sum rate improvement compared to traditional WMMSE, exceeding 20% under appropriate simulation conditions
Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction
Authors: Yan Chen, Cheng Liu
Date: 2025-03-22 00:00:00
Tasks: Causal Inference, Computational Efficiency, Transfer Learning

Accurate prediction of Remaining Useful Life (RUL) for complex industrial machinery is critical for the reliability and maintenance of mechatronic systems, but it is challenged by high-dimensional, noisy sensor data. We propose the Causal-Informed Data Pruning Framework (CIDPF), which pioneers the use of causal inference to identify sensor signals with robust causal relationships to RUL through PCMCI-based stability analysis, while a Gaussian Mixture Model (GMM) screens for anomalies. By training on only 10% of the pruned data, CIDPF fine-tunes pre-trained Large Language Models (LLMs) using parameter-efficient strategies, reducing training time by 90% compared to traditional approaches. Experiments on the N-CMAPSS dataset demonstrate that CIDPF achieves a 26% lower RMSE than existing methods and a 25% improvement over full-data baselines, showcasing superior accuracy and computational efficiency in industrial mechatronic systems. The framework's adaptability to multi-condition scenarios further underscores its practicality for industrial deployment.
DGAR: A Unified Domain Generalization Framework for RF-Enabled Human Activity Recognition
Authors: Junshuo Liu, Xin Shi, Robert C. Qiu
Date: 2025-03-22 00:00:00
Tasks: Activity Recognition, Domain Generalization, Human Activity Recognition, Privacy Preserving

Radio-frequency (RF)-based human activity recognition (HAR) provides a contactless and privacy-preserving solution for monitoring human behavior in applications such as personalized healthcare, ambient assisted living, and telemedicine. However, real-world deployment is frequently challenged by domain shifts arising from inter-subject variability, heterogeneous physical environments, and unseen activity patterns, resulting in significant performance degradation. To address this issue, we propose DGAR, a domain-generalized activity recognition framework that learns transferable representations without access to target-domain data. DGAR integrates instance-adaptive feature modulation with cross-domain distribution alignment to enhance both personalization and generalization. Specifically, it incorporates a squeeze-and-excitation (SE) block to extract salient spatiotemporal features and employs correlation alignment to mitigate inter-domain discrepancies. Extensive experiments on three public RF-based datasets -- HUST-HAR, Lab-LFM, and Office-LFM -- demonstrate that DGAR consistently outperforms state-of-the-art baselines, achieving up to a 5.81% improvement in weighted F1-score. These results underscore DGAR's potential to enable generalizable, real-time RF sensing systems in dynamic and personalized healthcare scenarios.