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—In this paper, we propose and study the distributed blind adaptive algorithms for wireless sensor network applications. Specifically, we derive distributed forms of the blind least mean square (LMS) and recursive least square (RLS) algorithms based on the constant modulus (CM) criterion. We assume that the inter-sensor communication is single-hop with(More)
We study the problem of distributed adaptive estimation over networks where nodes cooperate to estimate physical parameters that can vary over both space and time domains. We use a set of basis functions to characterize the space-varying nature of the parameters and propose a diffusion least mean-squares (LMS) strategy to recover these parameters from(More)
In this paper, we propose diffusion-based least mean square (LMS) algorithms that are robust against fading phenomena in wireless channels. The proposed algorithms, developed by combining diffusion LMS and classical estimation approaches, are able to estimate and update the underlying system parameters at each node by exploiting the sensor measurements and(More)
We study distributed least-mean square (LMS) estimation problems over adaptive networks, where nodes cooperatively work to estimate and track common parameters of an unknown system. We consider a scenario where the input and output response signals of the unknown system are both contaminated by measurement noise. In this case, if standard distributed(More)
—We propose a modified diffusion strategy for parameter estimation in sensor networks where nodes exchange information over fading wireless channels. We show that the effect of fading can be mitigated by incorporating local equalization coefficients into the diffusion process. We explain how the equalization coefficients are chosen and show that the (mean)(More)
We investigate the performance of distributed least-mean square (LMS) algorithms for parameter estimation over sensor networks where the regression data of each node are corrupted by white measurement noise. Under this condition, we show that the estimates produced by distributed LMS algorithms will be biased if the regression noise is excluded from(More)
Sidelobes cancellation is challenging task in beamforming and beam steering in smart antenna systems. The high level of sidelobes can significantly degrade the system performance as well as antenna power efficiency. In this paper, we present the new decimal genetic algorithm to reduce the sidelobe and at the same time create the nulls toward interferers and(More)
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(More)
We propose a distributed least-mean squares (LMS) procedure based on a diffusion strategy for localization and tracking of mobile terminals in cellular networks. In the proposed algorithm, collaborating base stations measure two sets of parameters, namely, the received signal strength (RSS) and the signal propagation time (SPT) to estimate mobile locations.(More)
—We study the performance of centralized least mean-squares (CLMS) algorithms in wireless sensor networks where nodes transmit their data over fading channels to a central processing unit (e.g., fusion center or cluster head), for parameter estimation. Wireless channel impairments, including fading and path loss, distort the transmitted data, cause link(More)