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arXiv 2025年

作者： Yang, Li Rajab, Mirna El Shami, Abdallah Muhaidat, Sami Faculty of Business and Information Technology Ontario Tech University OshawaONL1G 0C5 Canada Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates

Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational efficiency, support intelligent decision-making, and ensure effective resource allocation. However, the implementation of ZTNs is subject to security challenges that need to be resolved to achieve their full potential. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models. In this survey paper, we provide a comprehensive review of current security issues in ZTNs, emphasizing the need for advanced AI/ML-based security mechanisms that require minimal human intervention and protect AI/ML models themselves. Furthermore, we explore the potential of Automated ML (AutoML) technologies in developing robust security solutions for ZTNs. Through case studies, we illustrate practical approaches to securing ZTNs against both conventional and AI/ML-specific threats, including the development of autonomous intrusion detection systems and strategies to combat Adversarial ML (AML) attacks. The paper concludes with a discussion of the future research directions for the development of ZTN security *** Codes 68T01, 90C31 Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

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Physical Layer Security for Pinching-Antenna Systems (PASS)

arXiv

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arXiv 2025年

作者： Sun, Mingjun Ouyang, Chongjun Wu, Shaochuan Liu, Yuanwei School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

The pinching-antenna system (PASS) introduces new degrees of freedom (DoFs) for physical layer security (PLS) through pinching beamforming. In this paper, a couple of scenarios for secure beamforming for PASS are studied. 1) For the case with a single legitimate user (Bob) and a single eavesdropper (Eve), a closed-form expression for the optimal baseband beamformer is derived. On this basis, a gradient-based method is proposed to optimize the activated positions of pinching antennas (PAs). 2) For the case with multiple Bobs and multiple Eves, a fractional programming (FP)-based block coordinate descent (BCD) algorithm, termed FP-BCD, is proposed for optimizing the weighted secrecy sum-rate (WSSR). Specifically, a closed-form baseband beamformer is obtained via Lagrange multiplier method. Furthermore, owing to the non-convex objective function exhibiting numerous stationary points, a low-complexity one-dimensional search is used to find a high-quality solution of the PAs’ activated locations. Numerical results are provided to demonstrate that: i) All proposed algorithms achieve stable convergence within a few iterations, ii) across all considered power ranges, the FP-BCD algorithm outperforms baseline methods using zero-forcing (ZF) and maximal-ratio transmission (MRT) beamforming in terms of the WSSR, and iii) PASS achieves a significantly higher secrecy rate than traditional fixed-antenna systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Supplemental information

arXiv

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arXiv 2025年

作者： Sunada, Satoshi Niiyama, Tomoaki Kanno, Kazutaka Nogami, Rin Röhm, André Awano, Takato Uchida, Atsushi Faculty of Mechanical Engineering Institute of Science and Engineering Kanazawa University Kakuma-machi Kanazawa Ishikawa920-1192 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama338-8570 Japan Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing the innate computational power of physical processes;however, training their weight parameters is computationally expensive. We propose a training approach for substantially reducing this training cost. Our training approach merges an optimal control method for continuous-time dynamical systems with a biologically plausible training method—direct feedback alignment. In addition to the reduction of training time, this approach achieves robust processing even under measurement errors and noise without requiring detailed system information. The effectiveness was numerically and experimentally verified in an optoelectronic delay system. Our approach significantly extends the range of physical systems practically usable as PNNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Esc-Cafnet: A Multimodal Brain Tumor Segmentation Network Based on Enhanced Separable Convolution and Context-Aware Fusion

SSRN

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SSRN 2025年

作者： Zhao, Mengyao Tang, Chaosheng Dai, Chendi Sun, Junding Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

(Aim) Brain tumors pose a significant threat to human health and overall well-being. Identifying tumor locations is crucial for image-guided radiotherapy, effective treatment planning, and preventing other disorders. Nevertheless, notable difficulties exist because of (i) the diverse shapes of organs and tumor tissue structures, (ii) low contrast between adjacent organs among patients, and (iii) high imaging similarity in magnetic resonance (MR). (Method) We present an efficient ESC-CAFNet segmentation approach based on deep learning to address these issues. Our approach utilizes 3D Depth-wise separable convolution in the encoder to extract the brain tumor's volumetric feature map from MR images. Further, we introduce an Enhanced Separable Convolution Module (ESCM) that (i) focuses on significant features and (ii) reduces parameter weights of irrelevant features in the feature map. We then feed the output feature map of the encoder to the Context-Aware Fusion Module (CAFM) at the network's bottleneck. By strengthening the relationship between the query matrix and the key-value matrix at the pixel level, our approach reduces the computational complexity of self-attention from quadratic to linear calculation. Additionally, detailed feature information on the lesion is obtained through multi-scale features. Finally, Deep Supervision is employed to facilitate the learning of dense feature representations from MR *** the proposed ESC-CAFNet can accurately locate organs and refine the boundary of tumor regions. (Results) The proposed ESC-CAFNet approach achieved Dice scores of 91.01%, 80.56%, and 83.51%, and Hausdorff Distances (95th percentile) of 5.04, 4.13, 6.32 for the WT, ET, and TC based on Testing set of BRATS 2019, respectively. Meanwhile, our ESC-CAFNet achieved the best average Dice score of 85.62% in the testing set of BRATS 2020. (Conclusion) The accurate segmentation and lightweight design of our ESC-CAFNet provide valuable guidance for clinicians in di

关键词： Magnetic resonance imaging

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication System

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IEEE Wireless Communications Letters 2025年

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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Deep Unfolding Neural Networks for Fluid Antenna-Enhanced Vehicular Communication

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IEEE Transactions on Vehicular technology 2025年

作者： Feng, Biqian Feng, Chenyuan Wong, Kai-Kit Quek, Tony Q. S. University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China EURECOM Sophia Antipolis 06410 France University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore

Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation FA-enabled communication systems. © 2025 IEEE.

关键词： Deep neural networks

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Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks

arXiv

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arXiv 2025年

作者： Yang, Li Naser, Shimaa Shami, Abdallah Muhaidat, Sami Ong, Lyndon Debbah, Mérouane Faculty of Business and Information Technology Ontario Tech University OshawaONL1G 0C5 Canada KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada Ciena HanoverMD21076 United States

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and *** Codes 68T01, 90C31 Copyrigh

关键词： Cybersecurity

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Precision-Adaptive Task Offloading and Resource Allocation for Efficient Positioning and Sensing in Near-Field IoV Systems

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IEEE Internet of Things Journal 2025年

作者： Dai, Cheng Bao, Song Chen, Songlin Garg, Sahil Kaddoum, Georges Sichuan University College of Computer Science Sichuan Chengdu China École de technologie supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India École de technologie supérieure MontréalQCH3C1K3 Canada King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, signific

关键词： Resource allocation

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