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Asynchronous Adaptation and Learning Over Networks—Part II: Performance Analysis

Publications associées (64)

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Combination weights for diffusion strategies with imperfect information exchange

Ali H. Sayed

Adaptive networks rely on in-network and collaborative processing among distributed agents to deliver enhanced performance in estimation and inference tasks. Information is exchanged among the nodes, usually over noisy links. This paper first investigates ...

IEEE2012

Beam coordination via diffusion adaptation over array networks

Ali H. Sayed

In this work, we consider a distributed beam coordination problem, where a collection of arrays are interconnected by a certain topology. The beamformers employ an adaptive diffusion strategy to compute the beamforming weight vectors by relying solely on c ...

IEEE2012

Mean-square analysis of continuous-time distributed estimation strategies

Ali H. Sayed

In this work, we study the mean-square-error performance of a diffusion strategy for continuous-time estimation over networks. We derive differential equations that describe the evolution of the mean and correlation of the weight-error vector, and provide ...

IEEE2012

Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation Over Adaptive Networks

Ali H. Sayed

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In th ...

IEEE2012

Performance Limits for Distributed Estimation Over LMS Adaptive Networks

Ali H. Sayed

In this work, we analyze the mean-square performance of different strategies for distributed estimation over least-mean-squares (LMS) adaptive networks. The results highlight some useful properties for distributed adaptation in comparison to fusion-based c ...

IEEE2012

Distributed pareto-optimal solutions via diffusion adaptation

Ali H. Sayed

We consider solving multi-objective optimization problems in a distributed manner over a network of nodes. The problem is equivalent to optimizing a global cost that is the sum of individual components. Diffusion adaptation enables the nodes to cooperate l ...

IEEE2012

Diffusion Bias-Compensated RLS Estimation Over Adaptive Networks

Ali H. Sayed

We study the problem of distributed least-squares estimation over ad hoc adaptive networks, where the nodes have a common objective to estimate and track a parameter vector. We consider the case where there is stationary additive colored noise on both the ...

IEEE2011

On the effects of topology and node distribution on learning over complex adaptive networks

Ali H. Sayed

Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network through their collaborations, as dictated by the network topology and ...

IEEE2011

Optimal combination rules for adaptation and learning over networks

Ali H. Sayed

Adaptive networks, consisting of a collection of nodes with learning abilities, are well-suited to solve distributed inference problems and to model various types of self-organized behavior observed in nature. One important issue in designing adaptive netw ...

IEEE2011

Performance limits of LMS-based adaptive networks

Ali H. Sayed

In this work we analyze the mean-square performance of different strategies for adaptation over two-node least-mean-squares (LMS) networks. The results highlight some interesting properties for adaptive networks in comparison to centralized solutions. The ...

IEEE2011