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Affine Projection Algorithm Over Acoustic Sensor Networks for Active Noise Control

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 448-461页

作者： Ferrer, Miguel de Diego, Maria Pinero, Gema Gonzalez, Alberto Univ Politecn Valencia UPV Inst Telecommun & Multimedia Applicat iTEAM Valencia 46022 Spain

Acoustic sensor networks (ASNs) are an effective solution to implement active noise control (ANC) systems by using distributed adaptive algorithms. On one hand, ASNs provide scalable systems where the signal processing load is distributed among the network nodes. On the other hand, their noise reduction performance is comparable to that of their respective centralized processing systems. In this sense, the distributed multiple error filtered-x least mean squares (DMEFxLMS) adaptive algorithm has shown to obtain the same performance than its centralized counterpart as long as there are no communications constraints in the underlying ASN. Regarding affine projection (AP) adaptive algorithms, some distributed approaches that are approximated versions of the multichannel filtered-x affine projection (MFxAP) algorithm have been previously proposed. These AP algorithms can efficiently share the processing load among the nodes, but at the expense of worsening their convergence properties. In this paper we develop the exact distributed multichannel filtered-x AP (EFxAP) algorithm, which obtains the same solution as that of the MFxAP algorithm as long as there are no communications constraints in the underlying ASN. In the EFxAP algorithm each node can compute a part or the entire inverse matrix needed by the centralized MFxAP algorithm. Thus, we propose three different strategies that obtain significant computational saving: 1) Gauss Elimination, 2) block LU factorization, and 3) matrix inversion lemma. As a result, each node computes only between 25%-60% of the number of multiplications required by the direct inversion of the matrix. Regarding the performance in transient and steady states, the EFxAP exhibits the fastest convergence and the highest noise level reduction for any size of the acoustic network and any projection order of the AP algorithm compared to the DMEFxLMS and two previously reported distributed AP algorithms.

关键词： Microphones Signal processing algorithms Loudspeakers Acoustics Approximation algorithms Actuators Convergence Active noise control acoustic sensor networks affine projection algorithm distributed algorithms adaptive filters

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Fully Dynamic Line Maintenance by Hybrid Programmable Matter 36

Fully Dynamic Line Maintenance by Hybrid Programmable Matter

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36th IEEE International Parallel and distributed Processing Symposium (IEEE IPDPS)

作者： Nokhanji, Nooshin Flocchini, Paola Santoro, Nicola Carleton Univ Sch Comp Sci Ottawa ON Canada Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (纸本)9781665497473

Motivated by the manipulation of nanoscale materials, recent investigations have focused on hybrid systems where passive elements incapable of movement, called tiles, are manipulated by one or more mobile entities, called robots, with limited computational capabilities. Like in most self-organizing systems, the fundamental concern is with the (geometric) shapes created by the position of the tiles;among them, the line is perhaps the most important. The existing investigations have focused on formation of the shape, but not on its reconfiguration following the failure of some of the tiles. In this paper, we study the problem of maintaining a line formation in presence of fully dynamic failures: any tile can stop functioning at any time. We show how this problem can be solved by a group of very simple robots, with the computational power of deterministic finite automata.

关键词： Shape formation fault-tolerance hybrid programmable matter line maintenance distributed algorithms self-assembly

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Wireless Power Transfer with Information Asymmetry: A Public Goods Perspective

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2021年第1期20卷 276-291页

作者： Zhang, Meng Huang, Jianwei Zhang, Rui Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore

Wireless power transfer (WPT) technology enables a cost-effective and sustainable energy supply in wireless networks. However, the broadcast nature of wireless signals makes them non-excludable public goods, which leads to potential free-riders among energy receivers. In this study, we formulate the wireless power provision problem as a public goods provision problem, aiming to maximize the social welfare of a system of an energy transmitter (ET) and all the energy users (EUs), while considering their heterogeneous valuations, private information, and self-interested behaviors. We propose a two-phase all-or-none scheme involving a low-complexity Power And Taxation (PAT) mechanism, which ensures voluntary participation, truthfulness, budget balance, and social optimality at every Nash equilibrium (NE). We propose a distributed PAT (D-PAT) algorithm to reach an NE, and prove its convergence by connecting the structure of NEs and that of the optimal solution to a related optimization problem. We further extend the analysis to a multi-channel system, which brings a further challenge of non-strictly concave agents' payoffs. We propose a Multi-Channel PAT (M-PAT) mechanism and a distributed M-PAT (D-MPAT) algorithm to address the challenge. Simulation results show that, our design is most beneficial when there are more EUs and more homogeneous channel gains.

关键词： Wireless communication Economics Resource management distributed algorithms Mobile computing Convergence Optimization Wireless power transfer public goods network economics mechanism design game theory Lindahl allocation

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Deterministic protocols in the SINR model without knowledge of coordinates

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2021年 115卷 121-145页

作者： Moses, William K., Jr. Vaya, Shailesh Indian Inst Technol Madras Dept Comp Sci & Engn Chennai 600036 Tamil Nadu India Xerox Res Ctr India Bangalore 560048 Karnataka India Technion Israel Inst Technol Fac Ind Engn & Management Haifa Israel

This work studies multi-broadcasting for the SINR (Signal-to-Interference-plus-Noise-Ratio) model, assuming a subset of nodes are initially awake, when each device only has access to knowledge about the total number of nodes in the network ti, the range from which each node's label is taken {1, ..., N}, and the label of the device itself. We assume no knowledge of the physical coordinates of devices and no knowledge of the neighborhood of each node. We present a deterministic protocol for this problem that runs in O(n lg N lgn) rounds. There is no known polytime deterministic algorithm for this setting. A lower bound of Omega(n lg N) rounds is known for deterministic broadcasting without local knowledge [30]. We also present algorithms to achieve multi-broadcast in O(n lgN) rounds and create a backbone in O(n lgN) rounds, assuming that all nodes are initially awake. (C) 2020 Elsevier Inc. All rights reserved.

关键词： distributed algorithms Multi-broadcast Non-spontaneous wakeup Backbone creation Signal-to-Interference-plus-Noise-Ratio model Wireless networks Deterministic algorithms Strongly selective family based dilution

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Design of distributed Optimization Algorithm Based on Unbalanced Directed Graph Communication Topology Diagram

Design of Distributed Optimization Algorithm Based on Unbala...

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Networking Systems of AI (INSAI), International Conference on

作者： Qinqin Xu Siqi Luo School of Automation Central South University Changsha China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

The design of distributed optimization algorithms presents a multi-agent domain problem, offering advantages over traditional centralized optimization algorithms by requiring less information and leveraging faster information transmission, making it suitable for various application scenarios in modern production and life. However, while most distributed optimization algorithms are designed based on balanced graphs, practical projects often involve unbalanced directed graphs in their communication topology. To address this, the paper proposes a distributed discrete-time algorithm based on the row random matrix method and Lagrange multiplier method, tailored for distributed optimization problems with equality constraints and locally closed convex set constraints specifically for strongly connected fixed unbalanced directed graphs. This approach tackles challenges associated with the original row random matrix method's difficulty in handling equality constraints, requiring the introduction of a multiplication subsystem, while ensuring convergence speed.

关键词： Network topology Directed graphs Production Information processing Topology distributed algorithms Artificial intelligence Optimization Convergence

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The Nash Equilibrium With Inertia in Population Games

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第12期66卷 5742-5755页

作者： Gentile, Basilio Paccagnan, Dario Ogunsula, Bolutife Lygeros, John Circuit Mind London SE1 0HS England Imperial Coll London Dept Comp London SW7 2AZ England Bloomberg LP London EC4N 4TQ England Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland

In the traditional game-theoretic set up, where agents select actions and experience corresponding utilities, a Nash equilibrium is a configuration where no agent can improve their utility by unilaterally switching to a different action. In this article, we introduce the novel notion of inertial Nash equilibrium to account for the fact that in many practical situations switching action does not come for free. Specifically, we consider a population game and introduce the coefficients c(ij) describing the cost an agent incurs by switching from action i to action j. We define an inertial Nash equilibrium as a distribution over the action space where no agent benefits in switching to a different action, while taking into account the cost of such switch. First, we show that the set of inertial Nash equilibria contains all the Nash equilibria, is in general nonconvex, and can be characterized as a solution to a variational inequality. We then argue that classical algorithms for computing Nash equilibria cannot be used in the presence of switching costs. Finally, we propose a better-response dynamics algorithm and prove its convergence to an inertial Nash equilibrium. We apply our results to study the taxi drivers' distribution in Hong Kong.

关键词： distributed algorithms games multiagent systems Nash equilibrium vehicle routing

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Event-Triggered Quantized Average Consensus via Ratios of Accumulated Values

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第3期66卷 1293-1300页

作者： Rikos, Apostolos, I Hadjicostis, Christoforos N. KTH Royal Inst Technol Div Decis & Control Syst SE-10044 Stockholm Sweden Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus

We study the distributed average consensus problem in multiagent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the nodes, each associated with some initial value, to obtain the average (or some value close to the average) of these initial values. In this article, we present and analyze a novel distributed averaging algorithm that operates exclusively on quantized values (specifically, the information stored, processed, and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed distributed averaging protocol on quantized values and show that its execution, on any time-invariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value (represented as the ratio of two quantized values) that is equal to the exact average.

关键词： Quantization (signal) Convergence distributed algorithms Protocols Topology Probabilistic logic Network topology Digraphs event-triggered distributed algorithms multiagent systems quantization quantized average consensus

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distributed competitive satellite optical burst switching mechanism for satellite networks with ultra-high link delays

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OPTICAL ENGINEERING 2022年第6期61卷 066106-066106页

作者： Zhao, Zhe Qiu, Zhiliang Pan, Weitao Sun, Hanwen Zheng, Ling Xidian Univ State Key Lab Integrated Serv Networks Network Switching Grp Xian Peoples R China China Acad Space Technol Xian Xian Peoples R China Xian Univ Posts & Telecommun Xian Peoples R China

The ultra-high link latency of the conventional central reservation switching method in satellite optical networks limits link utilization. In this study, a novel fully distributed competitive satellite optical burst switching (DC-SOBS) mechanism was proposed to overcome the drawbacks of the optical circuit switching method and achieve link multiplexing. Furthermore, the proposed method addressed the low link utilization problem caused by the high link latency of the central-control-related methods. The DC-SOBS mechanism in the ring network of six Geostationary Orbit (GEO) satellites improved link utilization by three orders of magnitude compared with the reservation method. A data conflict processing method was proposed to ensure that the same information was provided to various optical switching nodes, and the same algorithm was used to achieve a consistent scheduling result in each node. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.

关键词： satellite communication optical burst switching distributed algorithms conflict processing

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Sparse Matrix Multiplication in the Low-Bandwidth Model 22

Sparse Matrix Multiplication in the Low-Bandwidth Model

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34th ACM Symposium on Parallelism in algorithms and Architectures (SPAA)

作者： Gupta, Chetan Hirvonen, Juho Korhonen, Janne H. Studeny, Jan Suomela, Jukka Aalto Univ Helsinki Finland TU Berlin Berlin Germany

ISBN: (纸本)9781450391467

We study matrix multiplication in the low-bandwidth model: There are n computers, and we need to compute the product of two n x n matrices. Initially computer i knows row i of each input matrix. In one communication round each computer can send and receive one O(log n)-bit message. Eventually computer i has to output row i of the product matrix. We seek to understand the complexity of this problem in the uniformly sparse case: each row and column of each input matrix has at most d non-zeros and in the product matrix we only need to know the values of at most d elements in each row or column. This is exactly the setting that we have, e.g., when we apply matrix multiplication for triangle detection in graphs of maximum degree d. We focus on the supported setting: the structure of the matrices is known in advance;only the numerical values of nonzero elements are unknown. There is a trivial algorithm that solves the problem in O(d(2)) rounds, but for a large d, better algorithms are known to exist;in the moderately dense regime the problem can be solved inO(dn(1/3)) communication rounds, and for very large d, the dominant solution is the fast matrix multiplication algorithm using O(n(1.158)) communication rounds (for matrix multiplication over fields and rings supporting fast matrix multiplication). In this work we show that it is possible to overcome quadratic barrier for all values of d: we present an algorithm that solves the problem in O(d(1.907)) rounds for fields and rings supporting fast matrix multiplication and O(d(1.927)) rounds for semirings, independent of n.

关键词： distributed algorithms congested clique low-bandwidth model matrix multiplication

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A Two-Level ADMM Algorithm for AC OPF With Global Convergence Guarantees

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IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第6期36卷 5271-5281页

作者： Sun, Kaizhao Sun, Xu Andy Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

This paper proposes a two-level distributed algorithmic framework for solving the AC optimal power flow (OPF) problem with convergence guarantees. The presence of highly nonconvex constraints in OPF poses significant challenges to distributed algorithms based on the alternating direction method of multipliers (ADMM). In particular, convergence is not provably guaranteed for nonconvex network optimization problems like AC OPF. In order to overcome this difficulty, we propose a new distributed reformulation for AC OPF and a two-level ADMM algorithm that goes beyond the standard framework of ADMM. We establish the global convergence and iteration complexity of the proposed algorithm under mild assumptions. Extensive numerical experiments over some largest test cases from NESTA and PGLib-OPF (up to 30 000-bus systems) demonstrate advantages of the proposed algorithm over existing ADMM variants in terms of convergence, scalability, and robustness. Moreover, under appropriate parallel implementation, the proposed algorithm exhibits fast convergence comparable to or even better than the state-of-the-art centralized solver.

关键词： Convergence Convex functions Reactive power distributed algorithms Tuning Sun Complexity theory distributed optimization optimal power flow augmented Lagrangian method alternating direction method of multipliers

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