In this paper, we present a novel distributed affine projection algorithm (APA) to solve distributedestimation problem within dynamic diffusion networks. In addition, mean-square stability of the proposed algorithm i...
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ISBN:
(纸本)9781424427093
In this paper, we present a novel distributed affine projection algorithm (APA) to solve distributedestimation problem within dynamic diffusion networks. In addition, mean-square stability of the proposed algorithm is also studied through exploitation of the energy conservation approach due to Sayed. Simulations confirm that the novel algorithm achieves a greatly improved performance as compared with a noncooperative scheme.
In this letter, we study the problem of adaptive parameter estimation for distributed wireless sensor networks (WSNs), where the non-Gaussian impulsive noises in the communication links are considered. In such cases, ...
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In this letter, we study the problem of adaptive parameter estimation for distributed wireless sensor networks (WSNs), where the non-Gaussian impulsive noises in the communication links are considered. In such cases, if the traditional distributed collaborative strategy is still adopted, the estimation performance of the network will decline significantly. Aiming at this problem, we propose a new spatial-temporal minimum error random interaction strategy for the distributedestimation in the presence of impulsive link noises. Furthermore, the maximum correlation entropy criterion and the stochastic gradient descent method are used to update the combination factor so that the network has dynamic and real-time adaptability to the link noises. The proposed algorithm is also compared with the classic DLMS algorithm and a state-of-the-art algorithm designed for the impulsive link noises. Simulation results show that the proposed algorithm can not only reduce the network traffic effectively, but also be robust to the non-Gaussian link impulsive noises while maintaining its advantage over the non-cooperative algorithm and achieving the optimal estimation performance.
We consider density estimation for Besov spaces when each sample is quantized to only a limited number of bits. We provide a noninteractive adaptive estimator that exploits the sparsity of wavelet bases, along with a ...
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We consider density estimation for Besov spaces when each sample is quantized to only a limited number of bits. We provide a noninteractive adaptive estimator that exploits the sparsity of wavelet bases, along with a simulate-and-infer technique from parametric estimation under communication constraints. We show that our estimator is nearly rate-optimal by deriving minimax lower bounds that hold even when interactive protocols are allowed. Interestingly, while our wavelet-based estimator is almost rate-optimal for Sobolev spaces as well, it is unclear whether the standard Fourier basis, which arise naturally for those spaces, can be used to achieve the same performance.
In this paper, we consider the problem of estimating multiple parameter vectors over a sensor network in a multitasking framework and under temporally-correlated input conditions. For this, an efficient clustered mult...
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In this paper, we consider the problem of estimating multiple parameter vectors over a sensor network in a multitasking framework and under temporally-correlated input conditions. For this, an efficient clustered multitask diffusion affine projection algorithm (APA) is proposed that enjoys both intra-cluster and inter-cluster cooperation via diffusion. It is, however, shown that while collaboration in principle is a useful step to enhance the performance of a network, uncontrolled mode of inter-cluster collaboration can at times be detrimental to its convergence performance, especially near steady-state. To overcome this, a controlled form of inter-cluster collaboration is proposed by means of a control variable which helps in maintaining the collaboration in right direction. The proposed controlled multitask strategy attains improved performance in terms of both transient and steady-state mean square deviation (MSD) vis-a-vis existing algorithms, as also confirmed by simulation studies. We carry out a detailed performance analysis of the proposed algorithm, obtain stability bounds for its convergence in both mean and mean-square senses, and derive expressions for the network level MSD. Simulation results reveal that the proposed scheme performs consistently well even in the absence of cluster information.
In recent years, distributedadaptive processing has received much attention from both theoretical and practical aspects. One of the efficient cooperation structures in distributedadaptive processing is the diffusion...
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In recent years, distributedadaptive processing has received much attention from both theoretical and practical aspects. One of the efficient cooperation structures in distributedadaptive processing is the diffusion strategy, which provides a platform for the cooperation of nodes that run an adaptive algorithm, such as the least mean-squares (LMS) algorithm. Despite the studies that have been done on the diffusion-based LMS algorithm, the effect of deficient length on such structures has been overlooked. Accordingly, in this paper, we study the steady-state performance of the deficient length diffusion LMS algorithm. The results of this study show, in particular, that setting the tap length below its actual value leads to drastic degradation of the steady-state excess mean-square error (EMSE) and mean-square deviation (MSD) in diffusion adaptive networks. Furthermore, unlike the full-length case, where the steady-state MSD and EMSE decrease significantly with the step size reduction, this study shows that in the deficient-length scenario, there are no significant improvements in the steady-state performance by reducing the step size. Therefore, according to this study, the tap length plays a key role in diffusion adaptive networks since the performance deterioration due to deficient selection of tap length could not be compensated by an adjustment in the step size. Experiments exhibit a very good match between simulations and theory.
Multitask diffusion strategies are useful to estimate node-specific, or, multiple parameter vectors over a distributed network by exploiting inter-cluster and intra-cluster cooperation. During cooperation, all nodes t...
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ISBN:
(纸本)9781728103976
Multitask diffusion strategies are useful to estimate node-specific, or, multiple parameter vectors over a distributed network by exploiting inter-cluster and intra-cluster cooperation. During cooperation, all nodes transmit their intermediate estimates to their neighboring nodes, resulting in high energy consumption. In this paper, we propose a clustered multitask diffusion affine projection algorithm by transmitting only a subset of the entries of the intermediate estimate vectors among the neighboring nodes. The proposed algorithm, namely, clustered multitask partial diffusion affine projection algorithm provides a trade-off between the estimation performance and the required communication cost. Important results on convergence (in mean and mean square) of the proposed strategy are presented. Numerical simulations reveal that even though the estimation performance deteriorates somewhat as the number of coefficients transmitted to the neighboring nodes decreases, the degradation can be compensated to a large extent by a proportional increase in the magnitude of the regularization strength among the clusters.
distributedadaptive networks achieve better estimation performance by exploiting temporal as well as spatial diversity. In this paper, we consider the problem of estimating multiple optimal parameter vectors (also te...
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ISBN:
(纸本)9789881476852
distributedadaptive networks achieve better estimation performance by exploiting temporal as well as spatial diversity. In this paper, we consider the problem of estimating multiple optimal parameter vectors (also termed as tasks) under correlated input, over a sensor network, where the nodes within the same cluster are engaged in estimating a common optimum parameter vector in distributed manner. For this, we present an efficient multitask diffusion affine projection algorithm (APA). The proposed scheme uses a regularized term to promote similarity among the parameter vectors estimated by neighboring clusters. Usage of APA makes the algorithm robust against correlated input. We present important results on the mean and mean square convergence of the proposed strategy. Simulations are carried out to demonstrate the effectiveness of the proposed algorithm. Compared to the non-cooperative APA, the proposed multitask diffusion APA exhibits remarkably improved performance in terms of both convergence rate and steady-state MSD.
We develop a least mean-squares (LMS) diffusion strategy for sensor network applications where it is desired to estimate parameters of physical phenomena that vary over space. In particular, we consider a regression m...
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ISBN:
(纸本)9781467301831
We develop a least mean-squares (LMS) diffusion strategy for sensor network applications where it is desired to estimate parameters of physical phenomena that vary over space. In particular, we consider a regression model with space-varying parameters that captures the system dynamics over time and space. We use a set of basis functions such as sinusoids or B-spline functions to replace the space-variant (local) parameters with space-invariant (global) parameters, and then apply diffusion adaptation to estimate the global representation. We illustrate the performance of the algorithm via simulations.
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