pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Azimuth Sampling and Interpolation for Bistatic ISAC Setups
Authors: Alexander Felix, Silvio Mandelli, Marcus Henninger, Stephan ten Brink
Date: 2025-04-27 00:00:00
Tasks: ISAC

A key challenge in future 6G Integrated Sensing and Communications (ISAC) networks is to define the angular operations of transmitter and receiver, i.e., the sampling task of the angular domains, to acquire information about the environment. In this work we extend previous analysis for optimal angular sampling of monostatic setups to two-dimensional bistatic deployments, that are as important as the former in future ISAC cellular scenarios. Our approach overcomes the limitations of suboptimal prior art sampling and interpolation techniques, such as spline interpolation. We demonstrate that separating azimuth operations of the two transmit and receive arrays is optimal to sample the angular domain in an array-specific normalized angular frequency (NAF). This allows us to derive a loss-less reconstruction of the angular domain, enabling a more efficient and accurate sampling strategy for bistatic sensing applications compared to legacy approaches. As demonstrated by different Monte Carlo experiments, our approach enables future bistatic ISAC deployments with better performance compared to the other suboptimal solutions.
Near-Field Collinear Dipole Array Design with Dual-Polarization Operation for Wireless Power Transfer
Authors: Jiawang Li
Date: 2025-04-27 00:00:00

A large intelligent surface (LIS) is a promising approach to enhancing 6G performance in sub-10 GHz frequency bands. An analysis of the horizontal dipole linear array reveals that when the array size is sufficiently large, employing the conjugate phase method to excite the antenna elements results in a focal point along the central line of the array that gradually stabilizes at a constant value. The study evaluates the 3dB focal resolution generated by the dipole array for two polarizations. Additionally, the feasibility of generating circularly polarized focal points and the focal displacement under a limited array size are explored. Previous near-field focal synthesis methods primarily considered only the line-of-sight (LOS) channel. To enhance practicality, this study adopts a two-ray channel model to analyze near-field focal synthesis results in the presence of a reflected path. Both two polarizations are discussed separately, with design guidelines provided for array placement and focal point positioning. Finally, electromagnetic simulations are conducted within the linear array to validate the proposed design. These simulations highlight the capability of the horizontal dipole array configuration to be directly applied without significant coupling effects between elements. The proposed design guidelines lay a solid foundation for the application of LIS in 6G technology.
Meta-learning based Selective Fixed-filter Active Noise Control System with ResNet Classifier
Authors: Y. Xiao, M. Liu, D. Wei, L. Jian
Date: 2025-04-27 00:00:00
Tasks: Meta-Learning

The selective fixed-filter strategy is popular in industrial applications involving active noise control (ANC) technology, which circumvents the time-consuming online learning process by selecting the best-matched pre-trained control filter. However, the existing selective fixed-filter ANC (SFANC) based algorithms classify noises in frequency band, which is not a reasonable approach. Moreover, they pre-train the control filter utilizing only a single noise segment, leading to inaccurate estimation and undesirable noise cancellation performance when dealing with dynamically time-varying noise. Inspired by the applicability of meta-learning to various models utilizing gradient descent technique, this paper proposes a novel meta-learning based SFANC system, wherein the fixed-filters that may not be optimal for specific types of noises but can rapidly adapt to previously unseen noise conditions are pre-trained. To address the mismatch issue between meta-learning update methods and ANC requirements while enhancing the receptive field and convergence speed of control filters, a multiple-input batch processing strategy is utilized in pre-training. Simulations based on the common ESC-50 noise dataset are performed and demonstrate the superiorities of the proposed method in terms of classification accuracy, convergence speed, and steady-state noise cancellation.
Comparison Research of Millimeter-Wave/Infrared Co-aperture Reflector Antenna Systems Based on a Specialized Film
Authors: Zongze Li, Xinlong Yang, Yiming Zhao, Chen Yao, Hongda Lu, Yong liu
Date: 2025-04-27 00:00:00
Tasks: Cloud Detection, Retrieval

This paper presents a novel co-aperture reflector antenna operating in millimeter-wave (MMW) and infrared (IR) for cloud detection radar. The proposed design combines a back-fed dual-reflector antenna, an IR optical reflection system, and a specialize thin film with IR-reflective/MMW-transmissive properties. Simulations demonstrate a gain exceeding 50 dBi within 94 GHz plush or minus 500 MHz bandwidth, with 0.46{\deg} beamwidth in both azimuth (E-plane) and elevation (H-plane) and sidelobe levels below -25 dB. This co-aperture architecture addresses the limitations of standalone MMW and IR radars, enabling high-resolution cloud microphysical parameter retrieval while minimizing system footprint. The design lays a foundation for airborne/spaceborne multi-mode detection systems.
UAV-Aided Progressive Interference Source Localization Based on Improved Trust Region Optimization
Authors: Guochen Gu, Zhipeng Lin, Qiuming Zhu, Junchang Chen, Qihui Wu, Hongtao Duan, Yang Huang, Weizhi Zhong
Date: 2025-04-27 00:00:00

Trust region optimization-based received signal strength indicator (RSSI) interference source localization methods have been widely used in low-altitude research. However, these methods often converge to local optima in complex environments, degrading the positioning performance. This paper presents a novel unmanned aerial vehicle (UAV)-aided progressive interference source localization method based on improved trust region optimization. By combining the Levenberg-Marquardt (LM) algorithm with particle swarm optimization (PSO), our proposed method can effectively enhance the success rate of localization. We also propose a confidence quantification approach based on the UAV-to-ground channel model. This approach considers the surrounding environmental information of the sampling points and dynamically adjusts the weight of the sampling data during the data fusion. As a result, the overall positioning accuracy can be significantly improved. Experimental results demonstrate the proposed method can achieve high-precision interference source localization in noisy and interference-prone environments.
Coherent Source Enumeration with Compact ULAs
Authors: Dibakar Sil, Sunder Ram Krishnan, Kumar Vijay Mishra
Date: 2025-04-27 00:00:00

Source enumeration typically relies on subspace-based techniques that require accurate separation of signal and noise subspaces. However, prior works do not address coherent sources in small uniform linear arrays, where ambiguities arise in the spatial spectrum. We address this by decomposing the forward-backward smoothed covariance matrix into a sum of a rank-constrained Toeplitz matrix and a diagonal matrix with non-negative entries representing the signal and noise subspace, respectively. We solve the resulting non-convex optimization problem by proposing Toeplitz approach for rank-based target estimation (TARgEt) that employs the alternating direction method of multipliers. Numerical results on both synthetic and real-world datasets demonstrate the effectiveness and robustness of TARgEt over a recent subspace matching method and a related covariance matrix reconstruction approach.
ODE-Former for Mobile Channel Prediction: A Novel Learning Structure Leveraging The Physics Continuity
Authors: Zhuoran Xiao
Date: 2025-04-27 00:00:00
Tasks: Prediction

Obtaining accurate channel state information (CSI) is crucial and challenging for multiple-input multiple-output (MIMO) wireless communication systems. With the increasing antenna scale and user mobility, traditional channel estimation approaches suffer greatly from high signaling overhead and channel aging problems. By exploring the intrinsic correlation among a set of historical CSI instances, channel prediction is proven to increase the CSI accuracy while lowering the signaling overhead significantly. Existing works view this problem as a regular discrete sequence prediction task while ignoring the unique physics property of wireless channels. This letter proposes a novel former-like learning structure based on neural ordinary differential equations (NODEs) inclusively designed for accurate and flexible channel prediction. The proposed network aims to represent wireless channels' implicit physics spatial-temporal continuity by integrating the Neural ODE into a former-like learning structure. Our proposed method impeccably fits channel matrices' mathematics features and enjoys solid network interpretability. Experimental results show that the proposed learning approach outperforms existing methods from the perspective of accuracy, flexibility, and robustness.
A Tutorial on MIMO-OFDM ISAC: From Far-Field to Near-Field
Authors: Qianglong Dai, Yong Zeng, Huizhi Wang, Changsheng You, Chao Zhou, Hongqiang Cheng, Xiaoli Xu, Shi Jin, A. Lee Swindlehurst, Yonina C. Eldar, Robert Schober, Rui Zhang, Xiaohu You
Date: 2025-04-27 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

Integrated sensing and communication (ISAC) is one of the key usage scenarios for future sixth-generation (6G) mobile communication networks, where communication and sensing (C&S) services are simultaneously provided through shared wireless spectrum, signal processing modules, hardware, and network infrastructure. Such an integration is strengthened by the technology trends in 6G, such as denser network nodes, larger antenna arrays, wider bandwidths, higher frequency bands, and more efficient utilization of spectrum and hardware resources, which incentivize and empower enhanced sensing capabilities. As the dominant waveform used in contemporary communication systems, orthogonal frequency division multiplexing (OFDM) is still expected to be a very competitive technology for 6G, rendering it necessary to thoroughly investigate the potential and challenges of OFDM ISAC. Thus, this paper aims to provide a comprehensive tutorial overview of ISAC systems enabled by large-scale multi-input multi-output (MIMO) and OFDM technologies and to discuss their fundamental principles, advantages, and enabling signal processing methods. To this end, a unified MIMO-OFDM ISAC system model is first introduced, followed by four frameworks for estimating parameters across the spatial, delay, and Doppler domains, including parallel one-domain, sequential one-domain, joint two-domain, and joint three-domain parameter estimation. Next, sensing algorithms and performance analyses are presented in detail for far-field scenarios where uniform plane wave (UPW) propagation is valid, followed by their extensions to near-field scenarios where uniform spherical wave (USW) characteristics need to be considered. Finally, this paper points out open challenges and outlines promising avenues for future research on MIMO-OFDM ISAC.
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers
Authors: Elad Sofer, Tomer Shaked, Caroline Chaux, Nir Shlezinger
Date: 2025-04-26 00:00:00
Tasks: Sensitivity

Machine learning (ML) models are often sensitive to carefully crafted yet seemingly unnoticeable perturbations. Such adversarial examples are considered to be a property of ML models, often associated with their black-box operation and sensitivity to features learned from data. This work examines the adversarial sensitivity of non-learned decision rules, and particularly of iterative optimizers. Our analysis is inspired by the recent developments in deep unfolding, which cast such optimizers as ML models. We show that non-learned iterative optimizers share the sensitivity to adversarial examples of ML models, and that attacking iterative optimizers effectively alters the optimization objective surface in a manner that modifies the minima sought. We then leverage the ability to cast iteration-limited optimizers as ML models to enhance robustness via adversarial training. For a class of proximal gradient optimizers, we rigorously prove how their learning affects adversarial sensitivity. We numerically back our findings, showing the vulnerability of various optimizers, as well as the robustness induced by unfolding and adversarial training.
Two-Agent DRL for Power Allocation and IRS Orientation in Dynamic NOMA-based OWC Networks
Authors: Ahrar N. Hamad, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2025-04-26 00:00:00
Tasks: Deep Reinforcement Learning, Fairness

Intelligent reflecting surfaces (IRSs) technology has been considered a promising solution in visible light communication (VLC) systems due to its potential to overcome the line-of-sight (LoS) blockage issue and enhance coverage. Moreover, integrating IRS with a downlink non-orthogonal multiple access (NOMA) transmission technique for multi-users is a smart solution to achieve a high sum rate and improve system performance. In this paper, a dynamic IRS-assisted NOMA-VLC system is modeled, and an optimization problem is formulated to maximize sum energy efficiency (SEE) and fairness among multiple mobile users under power allocation and IRS mirror orientation constraints. Due to the non-convex nature of the optimization problem and the non-linearity of the constraints, conventional optimization methods are impractical for real-time solutions. Therefore, a two-agent deep reinforcement learning (DRL) algorithm is designed for optimizing power allocation and IRS orientation based on centralized training with decentralized execution to obtain fast and real-time solutions in dynamic environments. The results show the superior performance of the proposed DRL algorithm compared to standard DRL algorithms typically used for resource allocation in wireless communication. The results also show that the proposed DRL algorithm achieves higher performance compared to deployments without IRS and with randomly oriented IRS elements.
BEM-Assisted Low-Complexity Channel Estimation for AFDM Systems over Doubly Selective Channels
Authors: Haowei Wu, Limin Liu, Jinglan Ou, Qihao Peng, Qu Luo, Pei Xiao, Rahim Tafazolli
Date: 2025-04-26 00:00:00

In this paper, we propose a low-complexity channel estimation scheme of affine frequency division multiplexing (AFDM) based on generalized complex exponential basis expansion model (GCE-BEM) over doubly selective channels. The GCE-BEM is used to solve fractional Doppler dispersion while significantly reducing the computational complexity of exhaustive search. Then, the closed-form expression of channel estimation error is derived for the minimum mean square error (MMSE) estimation algorithm. Based on the estimated channel, the MMSE detection is adopt to characterize the impacts of estimated channel on bit error rate (BER) by deriving the theoretical lower bound. Finally, numerical results demonstrate that the proposed scheme effectively mitigates severe inter-Doppler interference (IDoI). Our theoretical performance an alysis can perfectly match the Monte-Carlo results, validating the effectiveness of our proposed channel estimation based on GCE-BEM.
DMA Reception for Simultaneous Area-Wide Sensing and Multi-User Uplink Communications
Authors: Ioannis Gavras, George C. Alexandropoulos
Date: 2025-04-26 00:00:00

The recent surge in deploying extremely large antenna arrays is expected to play a vital role in future sixth generation wireless networks, enabling advanced radar target localization with enhanced angular and range resolution. This paper focuses on the promising technology of Dynamic Metasurface Antennas (DMAs), integrating numerous sub-wavelength-spaced metamaterials within a single aperture, and presents a novel framework for designing its analog reception beamforming weights with the goal to optimize sensing performance within a spatial Area of Interest (AoI), while simultaneously guaranteeing desired multi-user uplink communication performance. We derive the Cramer-Rao Bound (CRB) with DMA-based reception for both passive and active radar targets lying inside the AoI, which is then used as the optimization objective for configuring the discrete tunable phases of the metamaterials. Capitalizing on the DMA partially-connected architecture, we formulate the design problem as convex optimization and present both direct CRB minimization approaches and low complexity alternatives using a lower-bound approximation. Simulation results across various scenarios validate the effectiveness of the proposed framework, showing it consistently outperforms existing state-of-the-art methods.
Multi-Sensor Fusion of Active and Passive Measurements for Extended Object Tracking
Authors: Hong Zhu, Alexander Venus, Erik Leitinger, Klaus Witrisal
Date: 2025-04-25 00:00:00
Tasks: Object, Object Tracking, Sensor Fusion

This paper addresses the challenge of achieving robust and reliable positioning of a radio device carried by an agent, in scenarios where direct line-of-sight (LOS) radio links are obstructed by the agent. We propose a Bayesian estimation algorithm that integrates active measurements between the radio device and anchors with passive measurements in-between anchors reflecting off the agent. A geometry-based scattering measurement model is introduced for multi-sensor structures, and multiple object-related measurements are incorporated to formulate an extended object probabilistic data association (PDA) algorithm, where the agent that blocks, scatters and attenuates radio signals is modeled as an extended object (EO). The proposed approach significantly improves the accuracy during and after obstructed LOS conditions, outperforming the conventional PDA (which is based on the point-target-assumption) and methods relying solely on active measurements.
Discrete Codebook Design for Self-interference Suppression in mmWave ISAC
Authors: Guang Chai, Zhibin Yu, Thomas Wagner, Xiaofeng Wu, Giuseppe Caire
Date: 2025-04-22 00:00:00
Tasks: ISAC

This paper presents discrete codebook synthesis methods for self-interference (SI) suppression in a mmWave device, designed to support FD ISAC. We formulate a SINR maximization problem that optimizes the RX and TX codewords, aimed at suppressing the near-field SI signal while maintaining the beamforming gain in the far-field sensing directions. The formulation considers the practical constraints of discrete RX and TX codebooks with quantized phase settings, as well as a TX beamforming gain requirement in the specified communication direction. Under an alternating optimization framework, the RX and TX codewords are iteratively optimized, with one fixed while the other is optimized. When the TX codeword is fixed, we show that the RX codeword optimization problem can be formulated as an integer quadratic fractional programming (IQFP) problem. Using Dinkelbach's algorithm, we transform the problem into a sequence of subproblems in which the numerator and the denominator of the objective function are decoupled. These subproblems, subject to discrete constraints, are then efficiently solved by the spherical search (SS) method. This overall approach is referred to as FP-SS. When the RX codeword is fixed, the TX codeword optimization problem can similarly be formulated as an IQFP problem, whereas an additional TX beamforming constraint for communication needs to be considered. The problem is solved through Dinkelbach's transformation followed by the constrained spherical search (CSS), and we refer to this approach as FP-CSS. Finally, we integrate the FP-SS and FP-CSS methods into a joint RX-TX codebook design approach. Simulations show that, the proposed FP-SS and FP-CSS achieve the same SI suppression performance as the corresponding exhaustive search method, but with much lower complexity. Furthermore, the alternating optimization framework achieved even better SI suppression performance.
Generative AI for Physical-Layer Authentication
Authors: Rui Meng, Xiqi Cheng, Song Gao, Xiaodong Xu, Chen Dong, Guoshun Nan, Xiaofeng Tao, Ping Zhang, Tony Q. S. Quek
Date: 2025-04-25 00:00:00
Tasks: Data Augmentation, Denoising

In recent years, Artificial Intelligence (AI)-driven Physical-Layer Authentication (PLA), which focuses on achieving endogenous security and intelligent identity authentication, has attracted considerable interest. When compared with Discriminative AI (DAI), Generative AI (GAI) offers several advantages, such as fingerprint data augmentation, fingerprint denoising and reconstruction, and protection against adversarial attacks. Inspired by these innovations, this paper provides a systematic exploration of GAI's integration into PLA frameworks. We commence with a review of representative authentication techniques, emphasizing PLA's inherent strengths. Following this, we revisit four typical GAI models and contrast the limitations of DAI with the potential of GAI in addressing PLA challenges, including insufficient fingerprint data, environment noises and inferences, perturbations in fingerprint data, and complex tasks. Specifically, we delve into providing GAI-enhance methods for PLA across the data, model, and application layers in detail. Moreover, we present a case study that combines fingerprint extrapolation, generative diffusion models, and cooperative nodes to illustrate the superiority of GAI in bolstering the reliability of PLA compared to DAI. Additionally, we outline potential future research directions for GAI-based PLA.
Full-Duplex ISCC for Low-Altitude Networks: Resource Allocation and Coordinated Beamforming
Authors: Yiyang Chen, Wenchao Liu, Xuhui Zhang, Jinke Ren, Huijun Xing, Shuqiang Wang, Yanyan Shen
Date: 2025-04-25 00:00:00
Tasks: Edge-computing

This paper investigates an integrated sensing, communication, and computing system deployed over low-altitude networks for enabling applications within the low-altitude economy. In the considered system, a full-duplex enabled unmanned aerial vehicle (UAV) is dispatched in the airspace, functioning as a UAV-enabled low-altitude platform (ULAP). The ULAP is capable of achieving simultaneous information transmission, target sensing, and mobile edge computing services. To reduce the overall energy consumption of the system while meeting the sensing beampattern threshold and user computation requirements, we formulate an energy consumption minimization problem by jointly optimizing the task allocation, computation resource allocation, transmit beamforming, and receive beamforming. Since the problem is non-convex and involves highly coupled variables, we propose an efficient algorithm based on alternation optimization, which decomposes the original problem into tractable convex subproblems. Moreover, we analyze the convergence and complexity of the proposed algorithm. Numerical results demonstrate that the proposed scheme saves up to 54.12\% energy consumption performance compared to the benchmark schemes.