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Spatiotemporal-Enhanced Recurrent Neural Network for Network Traffic Prediction 28

Spatiotemporal-Enhanced Recurrent Neural Network for Network...

引用

28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Chen, Zhiyong Lai, Junyu Zhu, Junhong Ma, Wanyi Gan, Lianqiang Xia, Tian School of Aeronautics Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350300482

Network traffic prediction can serve as a proactive approach for network resource planning, allocation, and management. Besides, it can also be applied for load generation in digital twin networks (DTNs). This paper focuses on background traffic prediction of typical local area networks (LANs), which is vital for synchronous traffic generation in DTN. Conventional traffic prediction models are firstly reviewed. The challenges of DTN traffic prediction are analyzed. On that basis, a spatiotemporal-enhanced recurrent neural network (RNN) based approach is elaborated to accurately predict the background traffic matrices of target LANs. Experiments compare this proposed model with four baseline models, including LSTM, CNN-LSTM, ConvLSTM, and PredRNN. The results turn out that the spatiotemporal-enhanced RNN model outperforms the baselines on accuracy. In particular, it can decrease the MSE of PredRNN more than 18%, with acceptable efficiency degradation. © 2023 IEEE.

关键词： Long short-term memory

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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

引用

7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © The Author(s) 2025.

关键词： Federated learning

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Adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection 7th

Adaptive Communication Spectrum Sensing Algorithm Based on E...

引用

7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Shi, Weihong Tang, Tao Zhao, Runhui Wen, Hong Feng, Xuewei School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent loT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

Based on the traditional energy detection algorithm, an adaptive communication spectrum sensing algorithm based on energy detection is proposed for signals whose noise power is difficult to be estimated accurately. The algorithm takes into account the problem of large error in calculating the energy detection judgment threshold by noise power when the estimated value of noise power is inaccurate, and is able to perform adaptive communication spectrum sensing for signals with less a priori information and noise power that cannot be estimated accurately. Simulation results show that the adaptive communication spectrum sensing algorithm based on energy detection can effectively improve the performance of spectrum sensing for signals, and the algorithm can effectively detect and sense signals under the condition of reasonable setting of judgment coefficients. © The Author(s) 2025.

关键词： Adaptive Cognitive radio Energy detection Spectrum sensing

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Research on Modeling Methods for General Characteristics of Cyber-Physical Systems

Research on Modeling Methods for General Characteristics of ...

引用

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Zelin Li Yazhou Yue Yong Wang Fangfang Wu Dantao Zhang Xiangqiao Li National Key Laboratory of Science and Technology on Aircraft Control Avic Xi’An Flight Automatic Control Research Institute Xi’an China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

As a complex system that integrates computer science, network communication, and physical processes, it has shown huge potential in many fields. To effectively design, analyze and optimize CPS (Cyber-Physical Systems), establishing appropriate unified methods for modeling, simulation, and analysis of CPS is of crucial importance. In this paper, the concepts and characteristics of CPS are introduced, emphasizing the nature of its interdisciplinary and heterogeneous, as well as the extensive application of aviation, aerospace, vehicle, and other fields. And then existing CPS modeling methods are systematically reviewed and summarized from three perspectives: discrete modeling, continuous modeling, and advanced language modeling methods. The challenges and technical difficulties in CPS modeling process are identified, and three feasible solutions are proposed. The proposed solutions could contribute to the further study of CPS modeling.

关键词： Computer science Analytical models Automation Computational modeling Cyber-physical systems Complex systems

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Distributed Robust Fixed-Time Dynamic Average Consensus Protocol for Networked Agents 42

Distributed Robust Fixed-Time Dynamic Average Consensus Prot...

引用

42nd Chinese control Conference, CCC 2023

作者： Li, Weihao Shi, Mengji Ren, Zili Yue, Jiangfeng Lin, Boxian Qin, Kaiyu School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China The Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9789887581543

This paper seeks a robust dynamic average consensus protocol that makes the networked agents reach a common time-varying average state of multiple reference signals within an advanced preset time. It can be applied in fields such as multi-robot map merging and DC microgrid secondary control. Finite-time dynamic average consensus algorithms are studied in existing papers, while the convergence time is dependent on the initial states of the agents, so larger differences among their initial states may result in a slower convergence rate. Hence, a robust fixed-time dynamic average consensus scheme is presented in this paper. The settling time of the proposed scheme only relies on the parameters of the protocol and is independent of the initial values of agents. Specifically, the theoretical fixed-time convergence analysis is achieved by adopting the Lyapunov function, which indicates that dynamic average consensus can be reached within a fixed-time interval. Moreover, a damping term is added to the internal dynamics of agents to enhance the robustness of the protocol once the network interaction is changed. Finally, some numerical simulations are shown to verify the effectiveness of the proposed strategy. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Robust control

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Multi-Agent Deep Reinforcement Learning Based Computation Offloading Approach for LEO Satellite Broadband Networks 28

Multi-Agent Deep Reinforcement Learning Based Computation Of...

引用

28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Lai, Junyu Liu, Huashuo Sun, Yusong Zhu, Junhong Ma, Wanyi Gan, Lianqiang School of Aeronautics & Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350300482

Conventional computation offloading approaches are originally designed for ground networks, and are not effective for low earth orbit (LEO) satellite networks. This paper proposes a multi-agent deep reinforcement learning (MADRL) algorithm for making multi-level offloading decisions in LEO satellite networks. Offloading is formulated as a partially observable Markov decision process based multi-agent decision problem. Each satellite as an agent either conducts a received task, forwards it to neighbors, or sends it to ground clouds based on its own policy. These agents are independent and their deep neural networks to make offloading decisions share identical parameter values and are trained by using the same replay buffer. A centralized training and distributed executing mechanism is adopted to ensure that agents can make globally optimized offloading decisions. Comparative experiments demonstrate that the proposed MADRL algorithm outperforms the five baselines in terms of task processing delay and bandwidth consumption with acceptable computational complexity. © 2023 IEEE.

关键词： Reinforcement learning

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The Formation control Based on the Direction Coordination for Multiple Hypersonic Vehicles 42

The Formation Control Based on the Direction Coordination fo...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Yuqing Yu, Jianglong Ren, Zhang Dong, Xiwang Li, Qingdong Zhou, Yan Liang, Kaiqiang Wang, Junxiang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China China Academy of Launch Vehicle Technology Beijing100076 China

ISBN: (纸本)9789887581543

In this paper, a formation strategy based on the direction coordination and consistency of velocity and the optimization of the initial states is introduced for the formation of multiple hypersonic vehicles. Firstly, considering the mode of leader-follower, a two-dimensional controller is designed based on the dynamic model of hypersonic vehicle, which could realize the consistency of flight path angle and azimuth angle between followers and leader. Secondly, the initial states of the vehicles and the control parameters of the controller are taken as optimization variables, and the deformation of the formation during the whole flight is designed as the objective function. Finally, the combination of the PSACO (particle swarm ant colony optimization) algorithm and the SQP (sequential quadratic programming) algorithm is used to solve the optimization model. The simulation results show that the multiple hypersonic vehicles can achieve better formation flight based on the above methods. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Ant colony optimization

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An Offline Learning-Based Anti-interference Communication Scheme for UAV Networks 7th

An Offline Learning-Based Anti-interference Communication Sc...

引用

7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Tang, Tao Zhao, Runhui Wen, Hong Feng, Xuewei Shi, Weihong Peng, Yulin School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

Unmanned Aerial Vehicles (UAV) face challenges from advanced interference technologies, making them susceptible to malicious node attacks, data interception, and tampering. Traditional anti-interference decisions have limitations, as they cannot adaptively adjust to changes in interference signals. Moreover, anti-interference communication models based on Deep Reinforcement Learning (DRL) require prolonged interactions with the environment, demanding high requirements for anti-interference environments. This paper investigates an offline anti-interference decision based on Decision-Transformers, which can quickly and stably acquire practical anti-interference decision models. Simulation experiments have verified the effectiveness of this algorithm in making anti-interference decisions under AWGN and fading channel conditions. Furthermore, this offline approach can achieve the expected reward targets with fewer training iterations. © The Author(s) 2025.

关键词： Deep reinforcement learning

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Workspace Analysis and Cable Force Optimization of Cable-Driven Motion Simulation Platform 9

Workspace Analysis and Cable Force Optimization of Cable-Dri...

引用

9th International Conference on Fluid Power and Mechatronics, FPM 2023

作者： Huang, Haixiang Shang, Yaoxing Liu, Xiaochao Liu, Xiaoting Qian, Ran Bai, Ning School of Automation Science and Electrical Engineering Beihang University Beijing China Ningbo Institute of Technology Beihang University Ningbo China Tianmushan Laboratory Xixi Octagon City China Beihang University Science and Technology on Aircraft Control Laboratory Beijing China Research Institute for Frontier Science Beihang University Beijing China

ISBN: (纸本)9798350304947

The motion simulation platform is used to simulate the changes of vehicle attitude and position. The cable-driven motion simulation platform has a larger working space than the traditional rigidly connected motion simulation platform, but its configuration directly affects the system performance and working space range. In order to reveal the influence of the system configuration on the set of reachable positions of the motion platform, the solution of the force-closed working space and the force-feasible working space of the cable-driven motion simulation platform are carried out in this paper. For the over-constrained cable drive system, it is necessary to distribute the cable force to achieve the internal force, so this paper uses the minimum P-parameter of the relevant force as the optimized objective function to optimize the cable force solution of the motion simulation platform. The above work provides theoretical basis and reference for the design and application of the cable drive motion simulation platform. © 2023 IEEE.

关键词： Simulation platform

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Adaptive Flocking control of Uncertain Networked Agents for Tracking a Time-Varying Virtual Leader 13

Adaptive Flocking Control of Uncertain Networked Agents for ...

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13th IEEE International Conference on CYBER technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： You, Long Shi, Mengji Yue, Jiangfeng Li, Weihao Lin, Boxian Qin, Kaiyu School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350315196

This paper studies the adaptive flocking tracking problem of multi-agent systems. An adaptive flocking control scheme based on a radial basis neural network (RBFNN) is designed for multi-agent systems with nonlinear model uncertainties and a time-varying virtual leader. The model uncertainties are multifarious and the several states of the virtual leader are unknown. By introducing RBFNN with online updating of a weight matrix, the model uncertainties problem which may cause system destabilization is solved, and a distributed time-varying leader high-order information estimator is designed. Furthermore, the control protocol composed of the flocking control term, uncertainties compensation term, and state estimation term is proposed to accomplish adaptive flocking tracking control. The stability of the closed-loop system is proven based on the Lyapunov theory. Finally, the effectiveness of the proposed adaptive control scheme is verified by some simulation results. © 2023 IEEE.

关键词： Multi agent systems

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