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IEEE TRANSACTIONS ON signal processing 2010年第7期58卷 3765-3776页

作者： Ustebay, Deniz Oreshkin, Boris N. Coates, Mark J. Rabbat, Michael G. McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2A7 Canada

This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper, we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.

关键词： Applications of sensor networks average consensus distributed signal processing gossip algorithms

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distributed Covariance Estimation in Gaussian Graphical Models

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IEEE TRANSACTIONS ON signal processing 2012年第1期60卷 211-220页

作者： Wiesel, Ami Hero, Alfred O., III Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91905 Jerusalem Israel Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Hebrew Univ Jerusalem Rachel & Selim Benin Sch Comp Sci & Engn IL-91905 Jerusalem Israel

We consider distributed estimation of the inverse covariance matrix in Gaussian graphical models. These models factorize the multivariate distribution and allow for efficient distributed signal processing methods such as belief propagation (BP). The classical maximum likelihood approach to this covariance estimation problem, or potential function estimation in BP terminology, requires centralized computing and is computationally intensive. This motivates suboptimal distributed alternatives that tradeoff accuracy for communication cost. A natural solution is for each node to perform estimation of its local covariance with respect to its neighbors. The local maximum likelihood estimator is asymptotically consistent but suboptimal, i.e., it does not minimize mean squared estimation (MSE) error. We propose to improve the MSE performance by introducing additional symmetry constraints using averaging and pseudolikelihood estimation approaches. We compute the proposed estimates using message passing protocols, which can be efficiently implemented in large scale graphical models with many nodes. We illustrate the advantages of our proposed methods using numerical experiments with synthetic data as well as real world data from a wireless sensor network.

关键词： Covariance estimation distributed signal processing graphical models

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distributed beamforming for relay networks based on second-order statistics of the channel state information

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IEEE TRANSACTIONS ON signal processing 2008年第9期56卷 4306-4316页

作者： Havary-Nassab, Veria Shahbazpanahi, Shahram Grami, Ali Luo, Zhi-Quan Univ Ontario Inst Technol Fac Engn & Appl Sci Oshawa ON L1H 7K4 Canada Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

In this paper, the problem of distributed beam-forming is considered for a wireless network which consists of a transmitter, a receiver, and r relay nodes. For such a network, assuming that the second-order statistics of the channel coefficients are available, we study two different beamforming design approaches. As the first approach, we design the beamformer through minimization of the total transmit power subject to the receiver quality of service constraint. We show that this approach yields a closed-form solution. In the second approach, the beamforming weights are obtained through maximizing the receiver signal-to-noise ratio (SNR) subject to two different types of power constraints, namely the total transmit power constraint and individual relay power constraints. We show that the total power constraint leads to a closed-form solution while the individual relay power constraints result in a quadratic programming optimization problem. The later optimization problem does not have a closed-form solution. However, it is shown that using semidefinite relaxation, this problem can be turned into a convex feasibility semidefinite programming (SDP), and therefore, can be efficiently solved using interior point methods. Furthermore, we develop a simplified, thus suboptimal, technique which is computationally more efficient than the SDP approach. In fact, the simplified algorithm provides the beamforming weight vector in a closed form. Our numerical examples show that as the uncertainty in the channel state information is increased, satisfying the quality of service constraint becomes harder, i.e., it takes more power to satisfy these constraints. Also our simulation results show that when compared to the SDP-based method, our simplified technique suffers a 2-dB loss in SNR for low to moderate values of transmit power.

关键词： convex feasibility problem distributed beamforming distributed signal processing relay networks semidefinite programming

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distributed Combined Acoustic Echo Cancellation and Noise Reduction in Wireless Acoustic Sensor and Actuator Networks

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 534-547页

作者： Ruiz, Santiago van Waterschoot, Toon Moonen, Marc Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS B-3001 Leuven Belgium

The paper presents distributed algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor and actuator network (WASAN) where each node may have multiple microphones and multiple loudspeakers, and where the desired signal is a speech signal. A centralized integrated AEC and NR algorithm, i.e., multichannel Wiener filter (MWF), is used as starting point where echo signals are viewed as background noise signals and loudspeaker signals are used as additional input signals to the algorithm. By including prior knowledge (PK), namely that the loudspeaker signals do not contain any desired signal component, an alternative centralized cascade algorithm (PK-MWF) is obtained with an AEC stage first followed by an MWF-based NR stage which has a lower computational complexity. distributed algorithms can then be obtained from the MWF and PK-MWF algorithm, i.e., the generalized eigenvalue decomposition (GEVD)-based distributed adaptive node-specific signal estimation (DANSE) and PK-GEVD-DANSE algorithm, respectively. In the former, each node performs a reduced dimensional integrated AEC and NR algorithm and broadcasts only 1 fused signal (instead of all its signals) to the other nodes. In the PK-GEVD-DANSE algorithm, each node performs a reduced dimensional cascade AEC and NR algorithm and broadcasts only 2 fused signals (instead of all its signals) to the other nodes. The distributed algorithms achieve the same performance, upon convergence, as the corresponding centralized integrated (MWF) and centralized cascade (PK-MWF) algorithm. It is observed, however, that the communication cost in the PK-GEVD-DANSE algorithm can also be reduced, where each node then broadcasts only 1 fused signal (instead of 2 signals) to the other nodes. The resulting algorithm, referred to as the pruned PK-GEVD-DANSE (pPK-GEVD-DANSE) algorithm, then effectively combines the lowest possible communication cost (as low as in the GEVD-DANSE algorithm) wit

关键词： Loudspeakers signal processing algorithms Microphones Acoustics distributed algorithms Echo cancellers Speech processing distributed signal processing wireless acoustic sensor and actuator networks acoustic echo cancellation noise reduction multichannel Wiener filter

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An isotropic universal decentralized estimation scheme for a bandwidth constrained ad hoc sensor network

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2005年第4期23卷 735-744页

作者： Luo, ZQ Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

Consider a decentralized estimation problem whereby an ad hoe network of K distributed sensors wish to cooperate to estimate an unknown parameter over a bounded interval [-U, U-]. Each sensor collects one noise-corrupted sample, performs a local data quantization according to a fixed (but possibly probabilistic) rule,,and transmits the resulting discrete message to its neighbors. These discrete messages are then percolated in the network and used by each sensor to form its own minimum mean squared error (MMSE) estimate of the unknown parameter according to a fixed fusion rule. In this paper, we propose a simple probabilistic local quantization, rule: each sensor quantizes its observation to the first most significant bit (MSB) with probability 1/2, the second MSB with probability 1/4, and so on. Assuming the noises are uncorrelated and identically distributed across sensors and are bounded to [-U, U-], we show that this local quantization strategy together with a fusion rule can guarantee a MSE of 4U(2)/K, and that the average length of local messages is bounded (no more than 2.5 bits). Compared with the worst case Cramer-Rao lower bound of U-2/K (even for the centralized counterpart), this is within a factor of at most 4 to the minimum achievable MSE. Moreover, the proposed scheme is isotropic and universal in the sense that the local quantization rules and the final fusion rules are independent of sensor index, noise distribution, network size, or topology. In fact, the proposed scheme allows sensors in the network to operate identically and autonomously even when the network undergos changes in size or topology.

关键词： decentralized estimation distributed signal processing sensor network

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Multiscale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks

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IEEE TRANSACTIONS ON signal processing 2013年第9期61卷 2137-2149页

作者： Tsianos, Konstantinos I. Rabbat, Michael G. McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

This paper describes and analyzes a hierarchical algorithm called Multiscale Gossip for solving the distributed average consensus problem in wireless sensor networks. The algorithm proceeds by recursively partitioning a given network. Initially, nodes at the finest scale gossip to compute local averages. Then, using multi-hop communication and geographic routing to communicate between nodes that are not directly connected, these local averages are progressively fused up the hierarchy until the global average is computed. We show that the proposed hierarchical scheme with k = Theta(log log n) levels of hierarchy is competitive with state-of-the-art randomized gossip algorithms in terms of message complexity, achieving epsilon-accuracy with high probability after O(n log log n log 1/epsilon) single-hop messages. Key to our analysis is the way in which the network is recursively partitioned. We find that the above scaling law is achieved when subnetworks at scale j contain O(n((2/3)j)) nodes;then the message complexity at any individual scale is O(n log 1/epsilon) Another important consequence of the hierarchical construction is that the longest distance over which messages are exchanged is O(n(1/3)) hops (at the highest scale), and most messages (at lower scales) travel shorter distances. In networks that use link-level acknowledgements, this results in less congestion and resource usage by reducing message retransmissions. Simulations illustrate that the proposed scheme is more efficient than state-of-the-art randomized gossip algorithms based on averaging along paths.

关键词： Consensus algorithms distributed signal processing hierarchical processing scaling laws wireless sensor networks

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Universal decentralized estimation in a bandwidth constrained sensor network

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IEEE TRANSACTIONS ON INFORMATION THEORY 2005年第6期51卷 2210-2219页

作者： Luo, ZQ Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

Consider a situation where a set of distributed sensors and a fusion center wish to cooperate to estimate an unknown parameter over a bounded interval [-U, U]. Each sensor collects one noise-corrupted sample, performs a local estimation, and transmits a message to the fusion center, while the latter combines,the received messages to produce a final estimate. This correspondence investigates optimal local estimation and final fusion schemes under the constraint that the communication from each sensor to the fusion center must be a one-bit message. Such a binary message constraint is well motivated by the bandwidth limitation of the communication links, fusion center, and by the limited power budget of local sensors. In the absence of bandwidth constraint and assuming the noises are bounded to the interval [-U, U], additive, independent, but otherwise unknown, the classical estimation leory suggests that a total of O (u(2)/is an element of(2)) sensors are necessary and sufficient in order for the sensors and the fusion center to jointly estimate the unknown parameter within e root mean squared error (MSE). It is shown in this correspondence that the same remains true even with the binary message constraint. Furthermore, the optimal decentralized estimation scheme suggests allocating 1/2 of the sensors to estimate the first bit of the unknown parameter, 1/4 of the sensors to estimate the second bit, and so on.

关键词： decentralized estimation distributed signal processing sensor network

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Reduced-Dimension Linear Transform Coding of distributed Correlated signals With Incomplete Observations

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IEEE TRANSACTIONS ON INFORMATION THEORY 2009年第6期55卷 2848-2858页

作者： Nurdin, Hendra I. Mazumdar, Ravi R. Bagchi, Arunabha Australian Natl Univ Dept Informat Engn Coll Engn & Comp Sci Canberra ACT 0200 Australia Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Twente Dept Appl Math Syst Signals & Control Grp NL-7500 AE Enschede Netherlands

We study the problem of optimal reduced-dimension linear transform coding and reconstruction of a signal based on distributed correlated observations of the signal. In the mean square estimation context this involves finding the optimal signal representation based on multiple incomplete or only partial observations that are correlated. In particular, this leads to the study of finding the optimal Karhunen-Loeve basis based on the censored observations. The problem has been considered previously by Gastpar, Dragotti, and Vetterli in the context of jointly Gaussian random variables based on using conditional covariances. In this paper, we derive the estimation results in the more general setting of second-order random variables with arbitrary distributions, using entirely different techniques based on the idea of innovations. We explicitly solve the single transform coder case, give a characterization of optimality in the multiple distributed transform coders scenario and provide additional insights into the structure of the problem.

关键词： distributed signal processing innovations Karhunen-Loeve transform optimal linear estimation

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On the distributed Method of Multipliers for Separable Convex Optimization Problems

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2019年第3期5卷 495-510页

作者： Sherson, Thomas Heusdens, Richard Kleijn, W. Bastiaan Delft Univ Technol Dept Microelect Circuits & Syst Grp NL-2628 CD Delft Netherlands Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6012 New Zealand

In this paper, we present a novel method f o r convex optimization in distributed networks called the distributed method of multipliers (DMM). The proposed method is based on a combination of a particular dual lifting and classic monotone operator splitting approaches to produce an algorithm with guaranteed asymptotic convergence in undirected networks. The proposed method allows any separable convex problem with linear constraints to be solved in undirected networks. In contrast to typical distributed approaches, the structure of the network does not restrict the types of problems that can he solved. Furthermore, the solver can be applied to general separable problems, those with separable convex objectives and constraints, via the use of an additional primal lifting approach. Finally, we demonstrate the use of DMM in solving a number of classic signal processing problems including beamforming, channel capacity maximization and portfolio optimization.

关键词： distributed signal processing convex optimization monotone operator optimization over networks

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Consensus-based distributed dynamic sensor selection in decentralised sensor networks using the posterior Cramer-Rao lower bound

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signal processing 2015年 108卷 558-575页

作者： Mohammadi, Arash Asif, Amir Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 2J7 Canada Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 2W1 Canada

The paper considers the problem of dynamic sensor scheduling for non-linear tracking problems in distributed sensor/agent networks (AN/SN), where channel limitations restrict how many sensors can simultaneously participate in the estimation mechanism. Commonly referred to as sensor selection, the basic objective is to select a subset of sensors from available sensors that minimises the estimation error. The posterior Cramer-Rao lower bound (PCRLB) has recently been proposed as an effective sensor selection criteria. Nevertheless, existing PCRLB-based selectors are limited to centralised and hierarchical networks, and when extended to distributed architectures use approximate expressions. First, the non-conditional distributed PCRLB (dPCRLB) that considers observations and state variables to be random is used to derive the non-conditional sensor-selector. The dPCRLB expressions we derive are optimal. Unlike the non-conditional PCRLB, its conditional counterpart is a function of the past history of observations and is a more accurate representation of the system's performance. Second, the paper generalises the non-conditional dPCRLB sensor-selector to its conditional dPCRLB version. Both dPCRLB sensor-selectors use raw observations adding significant communication overhead. Third, the paper extends the conditional dPCRLB framework to quantised observations and develops the quantised version of the conditional dPCRLB sensor-selector. Numerical simulations verify the efficiency of our distributed dynamic sensor-selectors. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Consensus algorithms distributed signal processing Data fusion for non-linear dynamical systems Posterior Cramer-Rao lower bound (PCRLB) Sensor selection Wireless sensor networks

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