A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where the nodes are interested in estimating parameters that can be of local interest, common interest to a subset of ...
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A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where the nodes are interested in estimating parameters that can be of local interest, common interest to a subset of nodes and global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different, yet coupled Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.
A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest and parameters of global interest to the whole n...
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ISBN:
(纸本)9781479928934
A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local or global parameters. Although all the different LMS algorithms are coupled, the diffusion-based implementation of each LMS algorithm is exclusively undertaken by the nodes of the network interested in a specific set of local or global parameters. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.
We introduce an adaptive distributed technique that is suitable for parameterestimation in a network where nodes have different but overlapping interests. At each node, the parameters to be estimated can be of local ...
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We introduce an adaptive distributed technique that is suitable for parameterestimation in a network where nodes have different but overlapping interests. At each node, the parameters to be estimated can be of local interest, global interest to the whole network and common interest to a subset of nodes. To estimate each set of local, common and global parameters, a least mean squares (LMS) algorithm is implemented under an incremental mode of cooperation. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. Besides obtaining the conditions under which the proposed strategy converges in the mean to the solution of a centralized unit that processes all the observations, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node across the network. Finally, the theoretical results are validated through generic computer simulations as well as simulation results in the context of cooperative spectrum sensing in cognitive radio networks.
In this paper, we introduce an adaptive distributed technique that attains the exact (Recursive Least Squares) RLS solution of a node-specific parameter estimation problem where each node is interested in a set of par...
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ISBN:
(纸本)9781479936878
In this paper, we introduce an adaptive distributed technique that attains the exact (Recursive Least Squares) RLS solution of a node-specific parameter estimation problem where each node is interested in a set of parameters of local interest and a set of global parameters. To do so, each node of the network relies on its own local data as well as the communication with its intermediate neighbor under an incremental mode of cooperation. Since the required inter-node communication of the new scheme may be prohibitive for networks with scarce energy resources, an alternative low-cost scheme is derived to reduce the communication burden. It is shown that this approximate strategy may attain the exact RLS solution in the steady state. To illustrate the effectiveness of the proposed techniques we provide some indicative simulation results.
We introduce an adaptive distributed technique that is suitable for node-specific parameter estimation in an adaptive network where each node is interested in a set of parameters of local interest as well as a set of ...
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ISBN:
(纸本)9781479903566
We introduce an adaptive distributed technique that is suitable for node-specific parameter estimation in an adaptive network where each node is interested in a set of parameters of local interest as well as a set of network global parameters. The estimation of each set of parameters of local interest is undertaken by a local Least Mean Squares (LMS) algorithm at each node. At the same time and coupled with the previous local estimation processes, an incremental mode of cooperation is implemented at all nodes in order to perform an LMS algorithm which estimates the parameters of global interest. In the steady state, the new distributed technique converges to the MMSE solution of a centralized processor that is able to process all the observations. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.
In this paper, we introduce an adaptive distributed technique that attains the exact (Recursive Least Squares) RLS solution of a node-specific parameter estimation problem where each node is interested in a set of par...
详细信息
ISBN:
(纸本)9781479936878
In this paper, we introduce an adaptive distributed technique that attains the exact (Recursive Least Squares) RLS solution of a node-specific parameter estimation problem where each node is interested in a set of parameters of local interest and a set of global parameters. To do so, each node of the network relies on its own local data as well as the communication with its intermediate neighbor under an incremental mode of cooperation. Since the required inter-node communication of the new scheme may be prohibitive for networks with scarce energy resources, an alternative low-cost scheme is derived to reduce the communication burden. It is shown that this approximate strategy may attain the exact RLS solution in the steady state. To illustrate the effectiveness of the proposed techniques we provide some indicative simulation results.
We introduce an adaptive distributed technique that is suitable for node-specific parameter estimation in an adaptive network where each node is interested in a set of parameters of local interest as well as a set of ...
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ISBN:
(纸本)9781479903573
We introduce an adaptive distributed technique that is suitable for node-specific parameter estimation in an adaptive network where each node is interested in a set of parameters of local interest as well as a set of network global parameters. The estimation of each set of parameters of local interest is undertaken by a local Least Mean Squares (LMS) algorithm at each node. At the same time and coupled with the previous local estimation processes, an incremental mode of cooperation is implemented at all nodes in order to perform an LMS algorithm which estimates the parameters of global interest. In the steady state, the new distributed technique converges to the MMSE solution of a centralized processor that is able to process all the observations. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.
We study the node-specific parameter estimation problem, where agents in a network collaborate to obtain the different but overlapping vectors of parameters, which can be of local interest, common interest to a subset...
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We study the node-specific parameter estimation problem, where agents in a network collaborate to obtain the different but overlapping vectors of parameters, which can be of local interest, common interest to a subset of agents, and global interest to the whole network. We assume that all the regressors and the measurements are corrupted by additive noise. For these settings, a bias-compensation recursive-least-square algorithm based on a diffusion mode of cooperation is proposed;its stability is obtained via the detailed derivation of convergence in the mean sense. In addition, a closed-form expression for the algorithm's mean-square deviation is also provided to evaluate the steady-state performance of the whole network. Finally, we present simulation results that indicate the efficiency of the proposed method.
In this paper, we study the problem of node-specific parameter estimation(NSPE) over distributed multi-agent networks, whose nodes have noise-corrupted regressor vectors. When the classic diffusion least mean square(L...
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In this paper, we study the problem of node-specific parameter estimation(NSPE) over distributed multi-agent networks, whose nodes have noise-corrupted regressor vectors. When the classic diffusion least mean square(LMS) algorithm is used in this situation, it results biased estimates of the nodal objectives. Therefore, we propose an online bias-compensated method to remove the bias introduced on the diffusion LMS results. Moreover, we investigate performance analysis in the mean and mean-square sense. Furthermore, we provide numerical experiments to illustrate and compare the robustness of our method under various distributed strategies and different network topologies. (C) 2019 Elsevier B.V. All rights reserved.
In this paper, the parameterestimation problem based on diffusion least mean squares strategies is studied from a coalitional game theoretical perspective. The problem has been modeled as a non-transferable coalition...
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