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TechRxiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Software Engineering Sun Yat-Sen University Zhuhai China Department of Electrical and Computer Engineering Auburn University Auburn United States

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://***/Lizonghang/AGOD. © 2023, CC BY.

关键词： Reinforcement learning

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Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Huang, Huawei Mao, Shiwen The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Software Engineering Sun Yat-Sen University Zhuhai China The Department of Electrical and Computer Engineering Auburn University Auburn United States

关键词： Reinforcement learning

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Online Worker Selection Towards High Quality Map Collection for Autonomous Driving

Online Worker Selection Towards High Quality Map Collection ...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Xiaofeng Cao Yan Li Jiarong Han Peng Yang Feng Lyu Deke Guo Xuemin Shen Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha P.R. China School of Automation Beijing University of Posts and Telecommunications Beijing P.R. China Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Vehicle-based crowdsourcing is expected to be an economic yet efficient solution to build and maintain an accurate, fine-grained, and up-to-date environment map (i.e., high-definition map) for autonomous vehicles, which is an essential building block for safe and intelligent autonomous driving. However, how to select crowdsourcing workers with performance maximization is prudent and quite challenging since vehicles are highly dynamic and have unpredictable routes. In this paper, we study the worker selection problem for crowdsourced on-route map collection where the trade- off between the real-time worker exploration and exploitation is the main focus. Specifically, by adopting the multi-armed bandit model, we formulate a cumulative platform utility maximization problem. To solve this problem, we propose an Online Worker Selection (OWS) scheme, to learn drivers' performance and make worker selection decisions in real time. Essentially, two key designs are integrated in OWS: 1) performance transfer. If a new driver joins the crowdsourcing, we will initialize the new driver's performance based on the knowledge transferred from the existing drivers' records; and 2) marginal utility. Particularly, we carefully incorporate the platform utility to embody the marginal effect, i.e., repeated coverage by multiple vehicles on a certain road will undermine the utility. Based on the real-world vehicular GPS trace, we conduct extensive trace- driven simulations, and results demonstrate that our scheme can effectively obtain high-quality environment map, with on average 40.5% crowdsourcing utility gain over other benchmark schemes.

关键词： Crowdsourcing Roads Trajectory Task analysis Autonomous vehicles Global Positioning System

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Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

arXiv

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A unified leader-follower scheme for mobile robots with uncalibrated on-board camera

A unified leader-follower scheme for mobile robots with unca...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Dejun Guo Hesheng Wang Weidong Chen Ming Liu Zeyang Xia Kam K. Leang DARC (Design Automation Robotics & Control) Lab University of Utah Robotics Center Salt Lake City Utah USA State Key Laboratory of Robotics and System (HIT) Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong China Institute of Biomedical and Health Engineering Chinese Academy of Sciences Shenzhen China

This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme.

关键词： Cameras Robot vision systems Kinematics Mobile robots Calibration automation

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When Quantum information Technologies Meet Blockchain in Web 3.0

arXiv

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arXiv 2022年

作者： Xu, Minrui Ren, Xiaoxu Niyato, Dusit Kang, Jiawen Qiu, Chao Xiong, Zehui Wang, Xiaofei Leung, Victor C.M. School of Computer Science and Engineering Nanyang Technological University Singapore College of Intelligence and Computing Tianjin University Tianjin300072 China Shenzhen518000 China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

With the drive to create a decentralized digital economy, Web 3.0 has become a cornerstone of digital transformation, developed on the basis of computing-force networking, distributed data storage, and blockchain. With the rapid realization of quantum devices, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with the advantages of quantum computing and communication. Therefore, in this paper, we introduce a quantum blockchain-driven Web 3.0 framework that provides information-theoretic security for decentralized data transferring and payment transactions. First, we present the framework of quantum blockchain-driven Web 3.0 with future-proof security during the transmission of data and transaction information. Next, we discuss the potential applications and challenges of implementing quantum blockchain in Web 3.0. Finally, we describe a use case for quantum non-fungible tokens (NFTs) and propose a quantum deep learning-based optimal auction for NFT trading to maximize the achievable revenue for sufficient liquidity in Web 3.0. In this way, the proposed framework can achieve proven security and sustainability for the next-generation decentralized digital society. © 2022, CC BY.

关键词： Blockchain

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Improving the Transmission Rate by A Two-Phase Hybrid Duplex Scheme for Gaussian Relay Channel

Improving the Transmission Rate by A Two-Phase Hybrid Duplex...

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IEEE International Conference on Communications (ICC)

作者： Jianxin Duan Zhengchuan Chen Zhong Tian Min Wang Li Zhen Dapeng O. Wu Tony Q. S. Quek School of Microelectronics and Communication Engineering. Chongquing University Chongqing China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science City University of Hong Kong Hong Kong Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Combining half-duplex (HD) and full-duplex (FD) is promising in improving the information transmission rate of relay channels. This work proposes a novel two-phase hybrid duplex scheme for Gaussian relay channel where the relay operates in FD mode for a fraction of time and only transmits information for the rest of time. The achievable rate of the proposed hybrid duplex scheme is characterized in detail. Based on the obtained result, a joint time division and power allocation problem is formulated to maximize the achievable rate. In particular, the formulated problem is solved through a two-step optimization method. Firstly, the optimal relay power allocation is obtained for given time division factors. Then, the achievable rate maximization problem is addressed by finding the optimal time division factors. The closed-form expression for the maximal achievable rate is derived for some specific cases. Numerical results show that the proposed two-phase hybrid duplex scheme significantly improves the achievable rate of Gaussian relay channel compared with existing benchmark schemes.

关键词： Closed-form solutions Half-duplex system Optimization methods information processing Full-duplex system Benchmark testing Resource management

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Performance Comparison of Source Routing Tactics for WSN of Grid Topology

Performance Comparison of Source Routing Tactics for WSN of ...

引用

IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Wenxiang Li Yunhe Wu Chunsheng Zhu Yajie Ma Key Laboratory of Metallurgical Industry Process Systems in Hubei Province Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China

The nodes near sink in wireless sensor networks are featured with heavy transmission loads, so these performance bottleneck nodes are prone to running out of energy early with shortened network lifetime. Routing tactic plays important role on transmission performance and energy consumption of network. In this paper, we enumerate typical source routing tactics in Grid topology, include 1) Random Equal Probability Forwarding, 2) Line Forwarding, 3) Source-Zigzag Forwarding, 4) Destination-Zigzag Forwarding, and 5) Balance Forwarding. We analysis the traffic load distribution among nodes for these routing tactics, and calculate the load of performance bottleneck nodes and transmission delay in one data collection cycle. Based on the energy consumption model, we further make comparison on the energy consumptions of transmission and standing-by for these tactics. Numerical results show that Balance Forwarding tactic leads to the least overall energy consumption on performance bottleneck nodes, and Line Forwarding and Random Equal-Probability Forwarding lead to high energy consumption.

关键词： Routing Energy consumption Topology Delays Telecommunication traffic Network topology Educational institutions

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Index modulated OFDM with ICI self-cancellation in underwater acoustic communications

Index modulated OFDM with ICI self-cancellation in underwate...

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Asilomar Conference on Signals, systems & computers

作者： Miaowen Wen Yuke Li Xiang Cheng Liuqing Yang School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Department of Electrical and Computer Engineering Colorado State University Fort Collins CO USA

ISBN: (纸本)9781479982981

OFDM is promising for underwater acoustic (UWA) communications due to its potential to combat the large delay spread. However, the performance of the OFDM system is seriously deteriorated by the inter-carrier interference (ICI) caused by the transmitter/receiver motion and ocean waves. In this paper, we propose a novel ICI countermeasure. The key idea is to inactivate partial subcarriers according to index modulation and meanwhile transmit signals with opposite polarity on two adjacent active subcarriers for ICI self cancellation. Simulation results validate that the proposed scheme achieves much better bit error rate (BER) performance than many existing schemes in UWA communications.

关键词： OFDM Modulation Bit error rate Indexes Receivers Transmitters Sonar equipment

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Model-protected multi-task learning

arXiv

引用

arXiv 2018年

作者： Liang, Jian Liu, Ziqi Zhou, Jiayu Jiang, Xiaoqian Zhang, Changshui Wang, Fei Department of Automation Tsinghua University State Key Laboratory of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology Beijing100084 China Department of Computer Science Xi'an Jiaotong University Xi'an710049 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Department of Biomedical Informatics University of California San Diego San DiegoCA92093 United States Department of Healthcare Policy and Research Weill Cornell Medical College New York CityNY10065 United States

Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independently. MTL often leads to better trained models because they can leverage the commonalities among related tasks. However, because MTL algorithms can "leak" information from different models across different tasks, MTL poses a potential security risk. Specifically, an adversary may participate in the MTL process through one task and thereby acquire the model information for another task. The previously proposed privacy-preserving MTL methods protect data instances rather than models, and some of them may underperform in comparison with STL methods. In this paper, we propose a privacy-preserving MTL framework to prevent information from each model leaking to other models based on a perturbation of the covariance matrix of the model matrix. We study two popular MTL approaches for instantiation, namely, learning the low-rank and group-sparse patterns of the model matrix. Our algorithms can be guaranteed not to underperform compared with STL methods. We build our methods based upon tools for differential privacy, and privacy guarantees, utility bounds are provided, and heterogeneous privacy budgets are considered. The experiments demonstrate that our algorithms outperform the baseline methods constructed by existing privacy-preserving MTL methods on the proposed model-protection problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Covariance matrix

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