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Secure beamforming and deployment design for rate-splitting multiple access-based UAV communications

Science China(Information Sciences) 2025年第1期68卷 342-356页

作者： Bing WANG Xiaofeng TAO Shujun HAN Huici WU Kai YANG Zhu HAN National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Peng Cheng Laboratory Department of Electrical and Computer Engineering University of Houston

Rate-splitting multiple access(RSMA) has recently gained attention as a potential robust multiple access(MA)scheme for upcoming wireless networks. Given its ability to efficiently utilize wireless resources and design interference management strategies, it can be applied to unmanned aerial vehicle(UAV) networks to provide convenient services for large-scale access ground users. However, due to the line-of-sight(LoS) broadcast nature of UAV transmission, information is susceptible to eavesdropping in RSMA-based UAV networks. Moreover, the superposition of signals at the receiver in such networks becomes complicated. To cope with the challenge, we propose a two-user multi-input single-output(MISO) RSMA-based UAV secure transmission framework in downlink communication networks. In a passive eavesdropping scenario, our goal is to maximize the sum secrecy rate by optimizing the transmit beamforming and deployment location of the UAV-base station(UAV-BS),while considering quality-of-service(QoS) constraints, maximum transmit power, and flight space limitations. To address the non-convexity of the proposed problem, the optimization problem is first decoupled into two subproblems. Then, the successive convex approximation(SCA) method is employed to solve each subproblem using different propositions. In addition, an alternating optimization(AO)-based location RSMA(L-RSMA) beamforming algorithm is developed to implement joint optimization to obtain the suboptimal solution. Numerical results demonstrate that(1) the proposed L-RSMA scheme yields a28.97% higher sum secrecy rate than the baseline L-space division multiple access(SDMA) scheme;(2) the proposed L-RSMA scheme improves the security performance by 42.61% compared to the L-non-orthogonal multiple access(NOMA) scheme.

关键词： rate-splitting multiple access UAV communication precoding optimization deployment location physical layer security

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A Literature Review on Model Conversion, Inference, and Learning Strategies in EdgeML with TinyML Deployment

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computers, Materials & Continua 2025年第4期83卷 13-64页

作者： Muhammad Arif Muhammad Rashid Department of Computer Science and Artificial Intelligence Umm Al-Qura UniversityMakkah Al-Mukarama21955Saudi Arabia Department of Computer and Network Engineering Umm Al-Qura UniversityMakkah Al-Mukarama21955Saudi Arabia

Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s ***,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models ***,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various *** factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these ***,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using *** identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software *** the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning *** the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major *** a result,this article provides valuable insights for researchers,practitioners,and *** assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.

关键词： Edge machine learning tiny machine learning model compression inference learning algorithms

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A Base Station Deployment Algorithm for Wireless Positioning Considering Dynamic Obstacles

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computers, Materials & Continua 2025年第3期82卷 4573-4591页

作者： Aiguo Li Yunfei Jia Department of Network Engineering Xi’an University of Science and TechnologyXi’an710600China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710600China

In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind ***,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic ***,research in this area still needs to be *** paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this *** algorithm considers the dynamic alterations in obstacle locations within the designated *** determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage *** experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle *** results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles *** 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.

关键词： Wireless positioning base station deployment dynamic obstacles dynamic obstacle wireless positioning

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Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion 21st

Predicting from a Different Perspective: A Re-ranking Mode...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Iwamoto, Yuki Kaneiwa, Ken Department of Computer Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (纸本)9789819601240

Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models learn relation patterns as rules by utilizing subgraphs. Providing the same inputs with different rules leads to differences in the model’s predictions. In this paper, we focus on the behavior of such models. We propose a re-ranking-based model called ReDistLP (Re-ranking with a Distinct Model for Link Prediction). This model enhances the effectiveness of re-ranking by leveraging the difference in the predictions between the initial retriever and the re-ranker. ReDistLP outperforms the state-of-the-art methods in 2 out of 3 benchmarks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Intrusion Detection in IoT and IIoT: Comparing Lightweight Machine Learning Techniques Using TON_IoT, WUSTL-IIOT-2021, and EdgeIIoTset Datasets

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IEEE Access 2025年 13卷 73468-73485页

作者： Ismail, Shereen Dandan, Salah Qushou, Ala'a University of Michigan Merit Network Inc MI48108 United States University of North Dakota School of Electrical Engineering and Computer Science ND58202 United States University of Jordan Department of Computer Engineering Amman Jordan

The security of Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems has been significantly enhanced through the integration of effective intrusion detection systems (IDSs). Machine learning (ML) has emerged as a highly efficient approach for designing cyber-attack detection systems to improve the security. This study reviewed recent advancements in the literature utilizing the TON_IoT, WUSTL-IIoT-2021, and Edge-IIoTset datasets. A comprehensive performance analysis of various supervised ML classification techniques was conducted to identify lightweight models suitable for deployment in resource-constrained IoT and IIoT environments. The performance of Decision Tree (DT), Random Forest (RF), and three ensemble techniques: Bagging, Stacking, and LightGBM (LGBM), was evaluated. The TON_IoT, WUSTL-IIOT-2021, and Edge-IIoTset imbalanced datasets, representing three distinct IIoT environments and encompassing numerous samples of different attack types, were used. The impact of imbalanced class distributions on model performance was analyzed. The imbalanced datasets were customized for training and testing ML models, with feature selection performed using Mutual Information (MI). Model performance was assessed using several metrics: Precision, Recall, Micro-F1, Model Size, and Training Time. Furthermore, a cross-dataset transfer learning approach was applied to evaluate how models trained on the TON_IoT dataset generalize when tested on the WUSTL-IIoT-2021 dataset, demonstrating the ability of the models to generalize across datasets with common features and attack labels. For real-time intrusion detection and network traffic analysis, we set up an experiment to deploy the trained ML models in a live network environment. The experiment provided real-time insights into CPU usage, memory consumption, and network activity, with predictions continuously logged for monitoring and further analysis. © 2013 IEEE.

关键词： network intrusion

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A New Data-Free Backdoor Removal Method via Adversarial Self-Knowledge Distillation

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IEEE Internet of Things Journal 2025年第9期12卷 12267-12277页

作者： Li, Xuexiang Gao, Yafei Liu, Minglin Zhou, Xu Chen, Xianfu Wu, Celimuge Li, Jie Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China Michigan Technological University Department of Computer Science HoughtonMI49931 United States Shenzhen CyberAray Network Technology Company Ltd Shenzhen518038 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201204 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

In the context of Internet of Things edge devices, pretrained models are often sourced directly from cloud computing platforms due to the unavailability of training data. This lack of access during the training phase makes these models susceptible to backdoor attacks. To address this challenge, we introduce a novel data-free backdoor removal method that operates effectively even when only the poisoned model is accessible. Our innovative approach employs two end-to-end generators with identical architectures to create both clean and poisoned samples. These samples are crucial for transferring knowledge from the teacher model—the fixed poisoned model—to the student model, which is initialized with the poisoned model. Our method utilizes a channel shuffling technique during the distillation process to disrupt and eliminate the backdoor knowledge embedded in the teacher model. This process involves iterative updates of the generators and meticulous distillation of the student model, leading to efficient backdoor removal. We conducted extensive experiments on five sophisticated backdoor attacks across two benchmark datasets. The results demonstrate that our method not only significantly bolsters the model’s resistance to backdoor attacks but also maintains high recognition accuracy for clean samples, thereby outperforming existing methods. © 2014 IEEE.

关键词： Cloud platforms

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Coded Beam Training for RIS Assisted Wireless Communications

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3854-3867页

作者： Chen, Yuhao Dai, Linglong Department of Electronic Engineering China Tsinghua University State Key laboratory of Space Network and Communications Beijing100084 China Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States

Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for future 6G communications. To fully unleash the performance of RIS, accurate channel state information (CSI) is crucial. Beam training is widely utilized to acquire the CSI. However, before aligning the beam correctly to establish stable connections, the signal-to-noise ratio (SNR) at UE is inevitably low, which reduces the beam training accuracy. To deal with this problem, we exploit the coded beam training framework for RIS systems, which leverages the error correction capability of channel coding to improve the beam training accuracy under low SNR. Specifically, we first extend the coded beam training framework to RIS systems by decoupling the base station-RIS channel and the RIS-user channel. For this framework, codewords that accurately steer to multiple angles is essential for fully unleashing the error correction capability. In order to realize effective codeword design in RIS systems, we then propose a new codeword design criterion, based on which we propose a relaxed Gerchberg-Saxton (GS) based codeword design scheme by considering the constant modulus constraints of RIS elements. In addition, considering the two dimensional structure of RIS, we further propose a dimension reduced encoder design scheme, which can not only guarentee a better beam shape, but also enable a stronger error correction capability. Simulation results reveal that the proposed scheme can realize effective and accurate beam training in low SNR scenarios. © 2002-2012 IEEE.

关键词： Error correction

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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

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IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework

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IEEE Transactions on Signal Processing 2025年 73卷 1708-1726页

作者： Yu, Siyuan Chen, Wei Poor, H. Vincent Department of Electronic Engineering State Key Laboratory of Space Network and Communications and Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

Distributed stochastic gradient descent (SGD) has attracted considerable recent attention due to its potential for scaling computational resources, reducing training time, and helping protect user privacy in machine learning. However, stragglers and limited bandwidth may induce random computational/ communication delays, thereby severely hindering the learning process. Therefore, how to accelerate asynchronous SGD (ASGD) by efficiently scheduling multiple workers is an important issue. In this paper, a unified framework is presented to analyze and optimize the convergence of ASGD based on stochastic delay differential equations (SDDEs) and the Poisson approximation of aggregated gradient arrivals. In particular, we present the run time and staleness of distributed SGD without a memorylessness assumption on the computation times. Given the learning rate, we reveal the relevant SDDE's damping coefficient and its delay statistics, as functions of the number of activated clients, staleness threshold, the eigenvalues of the Hessian matrix of the objective function, and the overall computational/ communication delay. The formulated SDDE allows us to present both the distributed SGD's convergence condition and speed by calculating its characteristic roots, thereby optimizing the scheduling policies for asynchronous/event-triggered SGD. It is interestingly shown that increasing the number of activated workers does not necessarily accelerate distributed SGD due to staleness. Moreover, a small degree of staleness does not necessarily slow down the convergence, while a large degree of staleness will result in the divergence of distributed SGD. Numerical results demonstrate the potential of our SDDE framework, even in complex learning tasks with non-convex objective functions. © 2025 IEEE.

关键词： Eigenvalues and eigenfunctions

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A Hybrid EKF/WUFIR Filter for Indoor Localization Integrating INS and UWB Data

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IEEE Transactions on network Science and engineering 2025年第3期12卷 2266-2276页

作者： Cheng, Long Song, Jiahe Zhao, Wenhao Northeastern University Department of Computer and Communication Engineering Qinhuangdao066004 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China

Due to the complex and variable indoor environment, ultra-wideband (UWB) signal transmission is often obstructed by walls and obstacles, resulting in non-line-of-sight (NLOS), which reduces localization accuracy. Inertial navigation system (INS) is an autonomous navigation system that does not rely on external information and is not affected by NLOS. Therefore, a hybrid EKF/WUFIR filter indoor localization algorithm that integrates INS and UWB data is proposed. The proposed algorithm is composed of three parts: INS localization, UWB localization and data fusion. In the INS localization part, the motion model is used to determine the state of the target in real time using measurement data obtained from the inertial measurement unit (IMU). In the UWB localization part, a resettable residual weighted particle filter algorithm is proposed to mitigate the effect of NLOS on the localization results. In the data fusion part, a hybrid filtering algorithm combining extended Kalman filter (EKF) and weighted unbiased finite impulse response (WUFIR) filtering is proposed to fuse the INS and UWB localization data. Simulation and experimental results show that the proposed algorithm outperforms other comparative algorithms in terms of robustness and localization accuracy. © 2013 IEEE.

关键词： Inertial navigation systems

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