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distributed Optimization for Aggregative Games Based on Euler-Lagrange Systems With Large Delay Constraints

IEEE ACCESS 2020年 8卷 179272-179280页

作者： Zhang, Long Guo, Ge Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China

This article investigates an aggregative game based on Euler-Lagrange systems subject to time-varying communication delays. First, a distributed algorithm is put forward to try to find the Nash equilibrium by the deliberated group of Euler-Lagrange systems with "small" delay and "large" delay. Second, we illustrate the convergence of two circumstances, separately. The first circumstance derives the upper bound of delays for guaranteeing globally exponential convergence, and the other obtains globally exponential convergence, even in some restrictions on "large" delays. Finally, a numerical example is used to show the effectiveness and superiority of proposed method.

关键词： Delays Games Nash equilibrium Convergence Cost function Symmetric matrices distributed algorithms Aggregative games Nash equilibrium large delays Euler-Lagrange systems

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distributed subgraph finding: Progress and challenges 48

Distributed subgraph finding: Progress and challenges

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48th International Colloquium on Automata, Languages, and Programming, ICALP 2021

作者： Censor-Hillel, Keren Department of Computer Science Technion Haifa Israel

This is a survey of the exciting recent progress made in understanding the complexity of distributed subgraph finding problems. It overviews the results and techniques for assorted variants of subgraph finding problem... 详细信息

ISBN: (纸本)9783959771955

关键词： distributed algorithms Limited bandwidth Subgraph finding

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On Reliability Bound and Improvement of Sensing-Based Semipersistent Scheduling in LTE-V2X

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IEEE INTERNET OF THINGS JOURNAL 2021年第7期8卷 6101-6113页

作者： Zhang, Fenghui Wang, Michael Mao Deng, Ruilong Southeast Univ Sch Informat Sci & Engn Nanjing 211189 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ Sch Cyber Sci & Technol Hangzhou 310027 Peoples R China

After the sensing-based semipersistent scheduling (SPS) is introduced in the media access control layer of the long-term evolution vehicle-to-everything (LTE-V2X) Mode 4, many tests have been conducted to measure its performance. However, until now, there is still no clear mathematical expression for the reliability of this scheduling. To this end, in this article, we attempt to provide the lower and upper bounds of the reliability and propose a distributed algorithm for improving reliability. First, we give a mathematical description of sensing-based SPS and apply packet pass rate (PPR) to represent its reliability. Then, we analyze the SPS without sensing and present a theoretical expression of the reliability, which is described as a function of channel busy rate (CBR). Second, an iterative approach is applied to analyze the sensing-based SPS, and the mathematical expressions of lower and upper-reliability bounds are derived. Third, based on the existing condition of the upper bound, we propose a reliability improvement solution, which utilizes a distributed algorithm to process added information, such as the counter and the offset for the remaining storage space. Finally, this solution is applied to LTE-V2X, and simulation shows that the proposed scheme can significantly improve the scheduling reliability.

关键词： Sensors Reliability theory Upper bound distributed algorithms Dedicated short range communication Resource management Lower bound long-term evolution vehicle-to-everything (LTE-V2X) media access control (MAC) reliability sensing-based semipersistent scheduling (SPS) upper bound

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Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2021年第1期39卷 101-115页

作者： Shen, Yifei Shi, Yuanming Zhang, Jun Letaief, Khaled B. Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Peng Chong Lab Shenzhen 518066 Peoples R China

Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability and generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target task into the neural network architecture. In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis. Specifically, we first demonstrate that radio resource management problems can be formulated as graph optimization problems that enjoy a universal permutation equivariance property. We then identify a family of neural networks, named message passing graph neural networks (MPGNNs). It is demonstrated that they not only satisfy the permutation equivariance property, but also can generalize to large-scale problems, while enjoying a high computational efficiency. For interpretablity and theoretical guarantees, we prove the equivalence between MPGNNs and a family of distributed optimization algorithms, which is then used to analyze the performance and generalization of MPGNN-based methods. Extensive simulations, with power control and beamforming as two examples, demonstrate that the proposed method, trained in an unsupervised manner with unlabeled samples, matches or even outperforms classic optimization-based algorithms without domain-specific knowledge. Remarkably, the proposed method is highly scalable and can solve the beamforming problem in an interference channel with 1000 transceiver pairs within 6 milliseconds on a single GPU.

关键词： Wireless networks Neural networks Resource management Optimization Computer architecture Array signal processing Scalability Radio resource management wireless networks graph neural networks distributed algorithms permutation equivariance

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distributed optimisation approach to least-squares solution of Sylvester equations

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IET CONTROL THEORY AND APPLICATIONS 2020年第18期14卷 2968-2976页

作者： Deng, Wen Zeng, Xianlin Hong, Yiguang Univ Chinese Acad Sci Sch Math Sci Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China

In this study, the authors design distributed algorithms for solving the Sylvester equation AX+XB=C in the sense of least squares over a multi-agent network. In the problem setup, every agent in the interconnected system only has local information of some columns or rows of data matrices A, B and C, and exchanges information among neighbour agents. They propose algorithms with mainly focusing on a specific partition case, whose designs can be easily generalised to other partitions. Three distributed continuous-time algorithms aim at two cases for seeking a least-squares/regularisation solution from the viewpoint of optimisation. Due to the equivalence between an equilibrium point of each system under discussion and an optimal solution to the corresponding optimisation problem, the authors make use of semi-stability theory and methods in convex optimisation to prove convergence theorems of proposed algorithms that arrive at a least-squares/regularisation solution.

关键词： least squares approximations distributed control stability distributed algorithms continuous time systems matrix algebra convex programming multi-robot systems convex optimisation distributed optimisation approach least-squares solution Sylvester equations distributed algorithms multiagent network problem setup interconnected system local information data matrices neighbour agents continuous-time algorithms optimisation problem

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Minimum-Energy distributed Consensus Control of Multiagent Systems: A Network Approximation Approach

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第3期65卷 1144-1159页

作者： Chen, Fei Chen, Jie Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

This paper investigates the problem of minimizing the global energy cost for multiagent systems to achieve consensus over undirected network topologies. Conventional optimization problems so defined require all-to-all network topologies for interagent communications, which precludes the use of distributed control or otherwise demands that the network be complete. To circumvent this difficulty, we introduce a network approximation (NA) scheme in the optimization criterion, which tends to render the optimization problem to one seemingly over a complete graph network, thus removing the all-to-all communication requirement. With the NA cost, we show that a distributed optimal consensus algorithm always exists for any given connected network topology, which can be determined by solving a single-agent-level parametric algebraic Ricatti equation (PARE). We also investigate the performance of the optimal consensus algorithm, focusing on the minimal energy cost required to achieve consensus optimally, and the speed at which consensus is achieved. Furthermore, for certain more special yet worthy cases, such as single-integrator, double-integrator, and first-order unstable agents, we derive explicit expressions for the energy cost and the consensus speed. It can be seen from these results that the energy cost can be made arbitrarily small for single-integrator and double-integrator systems under the optimal distributed control. On the other hand, for the first-order unstable agents, the energy cost increases and consensus speed decreases monotonically with the value of the agent's real unstable pole.

关键词： Consensus algorithm Multi-agent systems distributed algorithms Network topology Optimization Optimal control Symmetric matrices Consensus speed energy cost multiagent systems network approximation (NA) parametric algebraic Ricatti equation (PARE)

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Upward Max-Min Fairness in Multipath High-Speed Networks

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IEEE ACCESS 2021年 9卷 163987-164005页

作者： Ludena-Gonzalez, Patricia Luis Lopez-Presa, Jose Diez Munoz, Fernando Univ Tecn Particular Loja Dept Ciencias Comp & Elect Loja 1101608 Ecuador Univ Politecn Madrid Dept Ingn Telemat & Elect Madrid 28031 Spain

To take full advantage of the available network capacity, connections need to be able to use multiple paths to route their packets. Max-min fairness (MMF) can be effectively applied to single-path networks, but computing MMF rates in multipath networks requires solving a series of linear programming (LP) problems with high computational cost. Thus, a relaxation of MMF has been proposed, namely, upward max-min fairness (UMMF), which can be solved by simple combinatorial algorithms. Current proposals carry out incremental approximations emulating the waterfilling algorithm, which inherently establishes a dependency between the time required to achieve the optimal solution and the capacity of the links. Thus, the more capacity the network has, the less efficient the algorithms are. We defined the concept of the saturation level as the basis for the computation of fair shares. We developed the first centralized algorithm based on this concept, which we call c-SLEN. Unlike its predecessors, its convergence time does not depend on network capacity, and it does not incur link oversaturation. Based on c-SLEN, we derived d-SLEN, a distributed protocol that does not need to maintain per-subflow information in routers and guarantees constant processing time for control packets, making it a good candidate for practical use. Finally, through extensive simulations, we showed that d-SLEN is faster, lighter, and more accurate than its counterparts. Owing to its accuracy and convergence speed, it is able to maintain the size of link queues at minimal values at all times, thus proactively avoiding network congestion.

关键词： Protocols distributed algorithms Convergence Throughput Routing Resource management Routing protocols ERC algorithms multipath networks upward max-min fairness high-speed networks proactive congestion control

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distributed Self-Stabilizing Capacitated Maximal Independent Set Construction in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2020年第4期114卷 3271-3293页

作者： Arapoglu, Ozkan Dagdeviren, Orhan Ege Univ Int Comp Inst Izmir Turkey

Wireless sensor networks (WSNs) are composed of a large number of wireless self-organized sensor nodes connected through a wireless decentralized distributed network without the aid of a predefined infrastructure. Fault-tolerance and power management are fundamental challenges in WSNs. A WSN is self-stabilizing if it can initially start at any state and obtain a legitimate state in a finite time without any external intervention. Self-stabilization is an important method for providing fault-tolerance in WSNs. Maximal independent set (MIS) is an extensively used structure for many important applications such as clustering (Randhawa and Jain in Wirel Personal Commun 97(3):3355, 2017. 10.1007/s11277-017-4674-5) and routing (Attea et al. in Wirel Personal Commun 81(2):819, 2015. 10.1007/s11277-014-2159-3;Lipinski in Wirel Personal Commun 101(1):251, 2018. 10.1007/s11277-018-5686-5) in WSNs. The capacitated MIS (CapMIS) problem is an extension of MIS in that each node has a capacity that determines the number of nodes it may dominate. In this paper, we propose a distributed self-stabilizing capacitated maximal independent set algorithm (CapMIS) in order to reduce energy consumption and support load balancing in WSNs. To the best of our knowledge, this is the first algorithm in this manner. The algorithm is validated through theoretical analysis as well as testbed implementations and simulations.

关键词： Capacitated maximal independent set Wireless sensor networks distributed algorithms Self-stabilizing algorithms

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A distributed Cooperative Control Algorithm for Optimal Power Flow and Voltage Regulation in DC Power System

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IEEE TRANSACTIONS ON POWER DELIVERY 2020年第2期35卷 892-903页

作者： Yang, Jian Feng, Wendong Hou, Xiaochao Xia, Qingping Zhang, Xin Wang, Peng Cent South Univ Sch Automat Hunan Prov Key Lab Power Elect Equipment & Grid Changsha 410083 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Power loss and voltage regulation are two fundamental and challenging issues in DC distributed generation system. In this paper, a multi-objective optimization problem is established to achieve trade-offs between reducing power loss and decreasing voltage deterioration by adjusting the weighting coefficients. This problem is proven to be a convex optimization under constant impedance load and constant current load. A distributed algorithm based on random coordinate descent method is developed to solve this problem without acquiring the information of line impedance. Thus, the optimal operating point of the system can be found automatically in real-time, and the deviation of optimal solution caused by the fluctuation of network parameters is avoided. In addition, the complexity of this algorithm is low and its computational requirement is reduced in practice. Finally, case studies are implemented to verify the effectiveness of the proposed algorithm under the scenarios of line parameter fluctuation, load variation, communication line failure, and plug-play capabilities.

关键词： Optimization Voltage control Load flow Impedance distributed algorithms Real-time systems DC distributed system distributed optimization random coordinate descent method voltage regulation power loss

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distributed C-Means Clustering Via Broadcast-Only Token Passing

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第1期7卷 315-325页

作者： Faramondi, Luca Oliva, Gabriele Setola, Roberto Hadjicostis, Christoforos N. Univ Campus Biomed Roma Dept Engn Unit Automat I-00128 Rome Italy Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus

This paper provides an implementation of the $C$-means algorithm in an asynchronous and distributed fashion;specifically, we consider a network of agents, each provided with a piece of information (e.g., data acquired via sensors) and we partition the agents in not mutually exclusive sets such that agents in the same set have similar information;moreover, each set of agents calculates a representative value for the group. Previous distributed algorithms that aimed at accomplishing this task have nontrivial demands, in that they require point-to-point communication capabilities among the agents, which may need to exchange large amounts of data in order to execute their computations. Within the proposed approach, instead, the agents need no prior knowledge about their neighbors and can simply communicate using broadcasts. The proposed solution consists in organizing data transmission following a token-passing approach, thus limiting the communication effort with respect to synchronous distributed implementations;furthermore, the token-passing phase is implemented via the broadcast-only communication, thus avoiding the requirements of the point-to-point communication. As shown via simulations, the latter feature is obtained at the cost of a modest increase in data transmission with respect to a traditional point-to-point token-passing scheme.

关键词： Asynchronous communication clustering algorithms distributed algorithms token networks

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