Diffusion recursive least-squares for distributed estimation over adaptive networks

By Federico S. Cattivelli, Cássio Guimarães Lopes, Ali H. Sayed. IEEE Transactions on Signal Processing, 2008

624 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/af83ff4ce14b1ac0e2ac5ebe392a84a850714110

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Citations in the corpus

Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks (2018, methodology): “Several diffusion-based distributed algorithms have been proposed such as the diffusion least mean square (dLMS) algorithm [6], diffusion conjugate gradient (dCG) [9], diffusion recursive least squares (dRLS) algorithm [7], and their modiﬁcations [10, 11, 12, 13, 14].”
Diffusion Least-Mean Squares With Adaptive Combiners: Formulation and Performance Analysis (2010, methodology): “In [12] and [14], ofﬂine optimization of the combination weights was proposed based on the steady-state performance analysis.”
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (2018, background): “One particular choice of the weights in C t that relies on only local information of the agents is known as Metropolis weights (Xiao et al., 2005; Cattivelli et al., 2008) (Boyd et al., 2006), broadcast gossip (Aysal et al., 2009), and network dropouts (Bianchi et al., 2013).”
Diffusion Strategies for Distributed Kalman Filtering and Smoothing (2010, methodology): “The diffusion Kalman filtering algorithm that is motivated here is based on extending our previous work on diffusion RLS filtering [15], [16] to the stochastic domain.”
Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior (2013, methodology): “We can also apply diffusion strategies to solve recursive least-squares and state-space estimation problems in a distributed manner [84], [36]; consensus-based solutions for state-space estimation appear in [85] and [86].”
Multitask Diffusion Adaptation Over Networks (2013, neutral): “Diffusion RLS over adaptive networks was studied in [28], [29].”
Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis (2008, background): “In doing so, an adaptive network structure is obtained where the structure as a whole is able to respond in real-time to the temporal and spatial variations in the statistical profile of the data [4]–[6].”
Diffusion Adaptation Strategies for Distributed Optimization and Learning Over Networks (2011, neutral): “, [9], [26]–[33], [36], [46], [52]–[55].”
Adaptive Networks (2014, methodology): “The ATC diffusion structure (49b), with adaptation preceding combination, appeared in the work [114] on adaptive distributed least squares schemes and also in the works [98] and [115]–[117] on distributed MSE and state–space estimation methods.”
Diffusion Adaptation over Networks (2012, background): “The mean-square performance and convergence of the diffusion RLS strategy are studied in some detail in [29].”
Diffusion LMS Strategies for Distributed Estimation (2010, methodology): “We further note that this particular ATC mode of cooperation with was originally proposed and studied in [8], [9] in the context of least-squares adaptive networks.”