Cassio G. Lopes

Diffusion Least-Mean Squares Over Adaptive Networks: Formulation and Performance Analysis

We formulate and study distributed estimation algorithms based on diffusion protocols to implement cooperation among individual adaptive nodes. The individual nodes are equipped with local learning abilities. They derive local estimates for the parameter of interest and share information with their neighbors only, giving rise to peer-to-peer protocols. The resulting algorithm is distributed, cooperative and able to respond in real time to changes in the environment. It improves performance in terms of transient and steady-state mean-square error, as compared with traditional noncooperative schemes. Closed-form expressions that describe the network performance in terms of mean-square error quantities are derived, presenting a very good match with simulations.

Incremental Adaptive Strategies Over Distributed Networks

An adaptive distributed strategy is developed based on incremental techniques. The proposed scheme addresses the problem of linear estimation in a cooperative fashion, in which nodes equipped with local computing abilities derive local estimates and share them with their predefined neighbors. The resulting algorithm is distributed, cooperative, and able to respond in real time to changes in the environment. Each node is allowed to communicate with its immediate neighbor in order to exploit the spatial dimension while limiting the communications burden at the same time. A spatial-temporal energy conservation argument is used to evaluate the steady-state performance of the individual nodes across the entire network. Computer simulations illustrate the results.

Diffusion recursive least-squares for distributed estimation over adaptive networks

We study the problem of distributed estimation over adaptive networks where a collection of nodes are required to estimate in a collaborative manner some parameter of interest from their measurements. The centralized solution to the problem uses a fusion center, thus, requiring a large amount of energy for communication. Incremental strategies that obtain the global solution have been proposed, but they require the definition of a cycle through the network. We propose a diffusion recursive least-squares algorithm where nodes need to communicate only with their closest neighbors. The algorithm has no topology constraints, and requires no transmission or inversion of matrices, therefore saving in communications and complexity. We show that the algorithm is stable and analyze its performance comparing it to the centralized global solution. We also show how to select the combination weights optimally.

Distributed Recursive Least-Squares Strategies Over Adaptive Networks

A distributed least-squares estimation strategy is developed by appealing to collaboration techniques that exploit the space-time structure of the data, achieving an exact recursive solution that is fully distributed. Each node is allowed to communicate with its immediate neighbor in order to exploit the spatial dimension, while it evolves locally to account for the time dimension as well. In applications where communication and energy resources are scarce, an approximate RLS scheme that is also fully distributed is proposed in order to decrease the communication burden necessary to implement distributed collaborative solution. The performance of the resulting algorithm tends to its exact counterpart in the mean-square sense as the forgetting factor lambda tends to unity. A spatial-temporal energy conservation argument is used to evaluate the steady-state performance of the individual nodes across the adaptive distributed network for the low communications RLS implementation. Computer simulations illustrate the results.

Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newtons method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.

Adaptive Processing over Distributed Networks

The article describes recent adaptive estimation algorithms over distributed networks. The algorithms rely on local collaborations and exploit the space-time structure of the data. Each node is allowed to communicate with its neighbors in order to exploit the spatial dimension, while it also evolves locally to account for the time dimension. Algorithms of the least-mean-squares and least-squares types are described. Both incremental and diffusion strategies are considered.

Diffusion Least-Mean Squares Over Adaptive Networks

Distributed adaptive algorithms are proposed to address the problem of estimation in distributed networks. We extend recent work by relying on static and adaptive diffusion strategies. The resulting adaptive networks are robust to node and link failures and present a substantial improvement over the non-cooperative case asserting that cooperation improves estimation performance. The distributed algorithms are peer-to-peer implementations suitable for networks with general topologies.

Diffusion adaptive networks with changing topologies

Adaptive networks (AN) have been recently proposed to address distributed estimation problems [1]-[4]. Here we extend prior work to changing topologies and data-normalized algorithms. The resulting framework may also treat signals with general distributions, rather than Gaussian, provided that certain data statistical moments are known. A byproduct of this formulation is a probabilistic diffusion adaptive network: a simpler yet robust variant of the standard diffusion algorithm [2].

Distributed Adaptive Incremental Strategies: Formulation and Performance Analysis

An adaptive distributed estimation strategy is developed based on incremental gradient techniques. The proposed scheme addresses the problem of distributed linear estimation in a cooperative fashion, resulting in a distributed algorithm that can respond in real time to changes in the environment. Each node is allowed to communicate only with its immediate neighbor in order to exploit the spatial dimension while at the same time reducing the communications burden. A spatial-temporal energy conservation argument is used to evaluate the steady-state mean-square-error performance of the individual nodes across the adaptive distributed network. Computer simulations illustrate the results

A diffusion rls scheme for distributed estimation over adaptive networks

We consider the problem of distributed estimation in adaptive networks where a collection of nodes are required to estimate in a collaborative manner some parameter of interest from their measurements. The centralized solution to the problem uses a fusion center, thus requiring a large amount of energy for communication. We propose a diffusion recursive least-squares algorithm where nodes need to communicate only with their closest neighbors. The algorithm has no topology constraints, and requires no transmission or inversion of matrices, therefore saving in communications and complexity. We show that the algorithm is stable and analyze its performance comparing it to the centralized global solution.