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IEEE Conference on Wireless Communications and Networking

作者： Zhe Li Lipeng Zhu Zhenyu Xiao Boyu Ning Daniel Benevides da Costa School of Electronic and Information Engineering Beihang University Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Movable antenna (MA) is an innovative technology that can enhance channel condition by altering antenna position within a local area. Integrating MA can further improve the performance of multiuser communications in non-orthogonal multiple access (NOMA) systems. In this paper, we investigate MA enabled NOMA for multiuser downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA enabled user terminals (UTs). An optimization problem is formulated to maximize the minimum achievable rate among all the UTs by jointly optimizing the positions of MAs of each UT, the precoding matrix at BS, and the successive interference cancellation (SIC) decoding indicator matrix at UTs, subject to the limited movement area of the MAs, the maximum transmit power of the BS, and the SIC decoding condition. To solve this non-convex problem, we combine the hippo optimization (HO) method with the alternating optimization (AO) method to obtain a suboptimal solution efficiently. Simulation results show that the proposed algorithm can significantly improve the rate performance of the NOMA system compared to the conventional FPA system as well as other benchmark schemes.

关键词： NOMA Interference cancellation Precoding Simulation Transmitting antennas Adaptive arrays Benchmark testing Downlink Decoding Optimization

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TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing

arXiv

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arXiv 2024年

作者： Sun, Zemin Sun, Geng Wu, Qingqing He, Long Liang, Shuang Pan, Hongyang Niyato, Dusit Yuen, Chau Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services in close proximity to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex and NP-hard mixed integer nonlinear programming (MINLP), we propose a two-timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach for solving the problem. In the short timescale, we propose a price-incentive model for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long timescale, we propose a convex optimization-based method for UAV trajectory control. Besides, we theoretically prove the stability, optimality, and polynomial complexity of TJCCT. Extended simulation results demonstrate that the proposed TJCCT outperforms the comparative algorithms in terms of the system utility, average processing rate, average completion delay, and average completion ratio. Copyright © 2024, The Authors. All rights reserved.

关键词： Computation offloading

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Secure Communication of Active RIS Assisted NOMA Networks

arXiv

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arXiv 2024年

作者： Li, Xuehua Pei, Yingjie Yue, Xinwei Liu, Yuanwei Ding, Zhiguo The Key Laboratory of Information and Communication Systems Ministry of Information Industry The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

As a revolutionary technology, reconfigurable intelligent surface (RIS) has been deemed as an indispensable part of the 6th generation communications due to its inherent ability to regulate the wireless channels. However, passive RIS (PRIS) still suffers from some pressing issues, one of which is that the fading of the entire reflection link is proportional to the product of the distances from the base station to the PRIS and from the PRIS to the users, i.e., the productive attenuation. To tackle this problem, active RIS (ARIS) has been proposed to reconfigure the wireless propagation condition and alleviate the productive attenuation. In this paper, we investigate the physical layer security of the ARIS assisted non-orthogonal multiple access (NOMA) networks with the attendance of external and internal eavesdroppers. To be specific, the closed-form expressions of secrecy outage probability (SOP) and secrecy system throughput are derived by invoking both imperfect successive interference cancellation (ipSIC) and perfect SIC. The secrecy diversity orders of legitimate users are obtained at high signal-to-noise ratios. Numerical results are presented to verify the accuracy of the theoretical expressions and indicate that: i) The SOP of ARIS assisted NOMA networks exceeds that of PRIS-NOMA, ARIS/PRIS-assisted orthogonal multiple access (OMA);ii) Due to the balance between the thermal noise and residual interference, introducing excess reconfigurable elements at ARIS is not helpful to reduce the SOP;and iii) The secrecy throughput performance of ARIS-NOMA networks outperforms that of PRIS-NOMA and ARIS/PRIS-OMA networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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A unified analytical method to quantify three types of fast frequency response from inverter-based resources

A unified analytical method to quantify three types of fast ...

引用

22nd Wind and Solar Integration Workshop (WIW 2023)

作者： S. Dong X. Fang J. Tan N. Gao X. Cui A. Hoke Power Systems Energy Center National Renewable Energy Laboratory Golden USA Department of Electrical and Computer Engineering Mississippi State University Starkville USA Department of Energy Science & Engineering Stanford University Stanford USA

With more inverter-based resources (IBRs), our power systems have lower frequency nadirs following N-1 contingencies, and undesired under-frequency load shedding (UFLS) can occur. To address this challenge, IBRs can be programmed to provide at least three types of fast frequency response (FFR), e.g., step response, proportional response (P/f droop response), and derivative response (synthetic inertia). However, these heterogeneous FFR challenge the study of power system frequency dynamics. Thus, this paper develops an analytical frequency nadir prediction method that allows for the consideration of all three potential forms of FFR provided by IBRs. The proposed method provides fast and accurate frequency nadir estimation after N-1 generation tripping contingencies. Our method is grounded on the closed-form solution for the frequency nadir, which is solved from the second-order system frequency response model considering the governor dynamics and three types of FFR. The simulation results in the IEEE 39-bus system with different types of FFR demonstrate that the proposed method provides an accurate and fast prediction of the frequency nadir under various disturbances.

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking and protein-peptide docking

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computers in Biology and Medicine 2024年 168卷 107727-107727页

作者： Cheng, Honglei Wang, Gai-Ge Chen, Liyan Wang, Rui School of Computer Science and Technology Ocean University of China Qingdao China Institute of Big Data and Information Technology Wenzhou University Wenzhou China College of Systems Engineering National University of Defense Technology Changsha China Xiangjiang Laboratory Changsha China

Multi-objective optimization problems (MOPs) are characterized as optimization problems in which multiple conflicting objective functions are optimized simultaneously. To solve MOPs, some algorithms used machine learning models to drive the evolutionary algorithms, leading to the design of a variety of model-based evolutionary algorithms. However, model collapse occurs during the generation of candidate solutions, which results in local optima and poor diversity in model-based evolutionary algorithms. To address this problem, we propose a dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA), where the dual-populations coordinate and cooperate to generate high-quality solutions, thus improving the performance of the evolutionary algorithm. We compare the proposed algorithm with the 7 state-of-the-art algorithms on 20 multi-objective benchmark functions. Experimental results indicate that DGMOEA achieves significant results in solving MOPs, where the metrics IGD and HV outperform the other comparative algorithms on 15 and 18 out of 20 benchmarks, respectively. Our algorithm is evaluated on the LEADS-PEP dataset containing 53 protein-peptide complexes, and the experimental results on solving the protein-peptide docking problem indicated that DGMOEA can effectively reduce the RMSD between the generated and the original peptide's 3D poses and achieve more competitive results. © 2023 Elsevier Ltd

关键词： Multiobjective optimization

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Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges

arXiv

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arXiv 2024年

作者： Chen, Nan Wang, Xiucheng Li, Zan Yin, Zhisheng Luan, Tom Shen, Xuemin The State Key Laboratory of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China School of Cyber Engineering Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada

This article presents a digital twin (DT)-enhanced reinforcement learning (RL) framework aimed at optimizing performance and reliability in network resource management, since the traditional RL methods face several unified challenges when applied to physical networks, including limited exploration efficiency, slow convergence, poor long-term performance, and safety concerns during the exploration phase. To deal with the above challenges, a comprehensive DT-based framework is proposed to enhance the convergence speed and performance for unified RL-based resource management. The proposed framework provides safe action exploration, more accurate estimates of long-term returns, faster training convergence, higher convergence performance, and real-time adaptation to varying network conditions. Then, two case studies on ultrareliable and low-latency communication (URLLC) services and multiple unmanned aerial vehicles (UAV) network are presented, demonstrating improvements of the proposed framework in performance, convergence speed, and training cost reduction both on traditional RL and neural network based Deep RL (DRL). Finally, the article identifies and explores some of the research challenges and open issues in this rapidly evolving field. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Stability Analysis of Connected Vehicle Platoons with Markovian Packet Losses

引用

IEEE Internet of Things Journal 2025年

作者： Wu, Yilin Zhao, Xiaoyan Shen, Zhiping Liu, Yonggui Li, Zeming Guangdong University of Education School of Computer Science Guangzhou510310 China Henan Normal University School of Mathematics and Statistics Xinxiang453007 China South China University of Technology Key Laboratory of Autonomous Systems and Network Control Ministry of Education The College of Automatic Science and Engineering Guangzhou510640 China

This article focuses on discussing the effects of inter-vehicular communication on the performance of platoon control. Different from the existing results established under a critical assumption that the fading channels are independent, this paper models the inter-vehicular communication as Markovian packet loss channel which can capture the temporal interdependencies of packet losses. Our aim is to decide whether there exists a distributed controller such that the underlying vehicle platoon can achieve mean square stability (MSS) over Markovian packet loss channels. First, for the scenario with identical Markovian packet losses, we begin by deriving a necessary and sufficient condition for the platoon MSS, which is established by utilizing the stability principles of Markov jump linear systems. Additionally, we propose a numerically verifiable criterion for MSS, which can be validated through the feasibility of linear matrix inequalities (LMIs). Furthermore, based on the modified Riccati inequality (MRI), we provide an analytical sufficient condition that ensures MSS for vehicle platoons. This condition is expressed in terms of the information flow topology (IFT) and the statistical parameters of Markov channels. Second, for the case with nonidentical packet dropouts, we introduce a Markovian loss channel based switching (MLCBS) model. Building upon this model, we further propose a sufficient conditon related to IFT and the transition probability matrix, and a distributed controller that is not only easy to implement but also enables the achievement of MSS for vehicle platoons with nonidentical packet dropouts. Ultimately, numerical experiments are performed to validate the obtained outcomes. © 2014 IEEE.

关键词： Intelligent Transportation systems Resource-Constrained Networks Vehicular Networks

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Denoised Internal Models:A Brain-inspired Autoencoder Against Adversarial Attacks

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Machine Intelligence Research 2022年第5期19卷 456-471页

作者： Kai-Yuan Liu Xing-Yu Li Yu-Rui Lai Hang Su Jia-Chen Wang Chun-Xu Guo Hong Xie Ji-Song Guan Yi Zhou School of Life Sciences and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201602China School of Life Sciences Tsinghua UniversityBeijing 100084China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-intelligence Nanophotonics School of Optical-electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest *** by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this *** the pipeline in the human brain for visual signal processing,DIM adopts a two-stage *** the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the *** by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each *** evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.

关键词： Brain-inspired learning autoencoder robustness adversarial attack generative model

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Improving routing efficiency in vehicular AD Hoc Networks

Improving routing efficiency in vehicular AD Hoc Networks

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： W. Akermi N. Alyaoui A. B. Guiloufi K. Chabbir N. Nasri Automatic Industrial Systems Department Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Preparatory Institute for Engineering Studies of Gabes Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science Department-Gafsa Gafsa University Gafsa Tunisia Laboratory of Smart Systems for Engineering and E-Health Based on Technologies of Image and Telecommunications (SETIT) Sfax University Sfax Tunisia

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Vehicular networks, also known as Vehicular Ad Hoc Networks (VANETs), have become a crucial technology for vehicle communication, providing wireless connectivity among vehicles and between vehicles and infrastructure. However, the dynamic and complex nature of these networks presents unique challenges in routing and resource management. In this study, we explore the use of artificial intelligence (AI) to improve the performance of vehicular networks, focusing on two widely used routing protocols: Optimized Link State Routing (OLSR) and Destination-Sequenced Distance Vector (DSDV).We aim to evaluate the performance of these traditional routing protocols within the context of vehicular networks by integrating machine learning techniques such as neural network regression (MLRegression). Our goal is to understand how the integration of artificial intelligence can enhance the prediction and optimization of vehicular network performance. Using MLRegression, we aim to develop accurate prediction models for performance metrics such as latency, throughput, packet delivery rate, and more, based on network parameters and environmental conditions. This comparative evaluation, which involves incorporating an AI-based approach, will allow us to better understand the strengths and weaknesses of each protocol and provide recommendations for improving vehicular networks.

关键词： Measurement Wireless communication Adaptation models Adaptive systems Vehicular ad hoc networks Routing Throughput Routing protocols Vectors Vehicle dynamics

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