Stable adaptive channel estimation method under impulsive noise environments

Gui, Guan; Zhang, Tingping; Dan, Jingpei; Xu, Li

doi:10.1002/dac.3104

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…These sensing devices can be used to detect a multitude of observations, from simple phenomena to complex events [29]- [33]. So many sensing devices sensing from surrounding environment, thus leading to an overwhelming amount of data for sensor-based network [26], [34]- [36]. The amount of data loaded by the sensor node is too large and the data need to be processed can cause many challenge issues for wireless communications [37]- [41].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Information Maximization Based Adaptive Congestion Control Scheme in Wireless Sensor Network

et al. 2019

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Intelligent communication technology can enhance user experience and improve people's lifestyle. However, due to the limited resources of wireless sensor networks (WSNs), the congestion occurs when the node's traffic load exceeds it's the available capacity. Congestion may cause serious problems such as high packet loss and low throughput, which is an extremely deleterious impact on the performance of WSNs. To tackle this issue, a differentiated rate control data collection (DRCDC) scheme is proposed to avoid or alleviate the network congestion. Different from the previous scheme, the DRCDC scheme can mitigate the congestion by intelligently reducing the data that contains less information, and maintain the distortion rate of information collected by the network is small. The DRCDC mainly avoids or mitigates congestion on the spatial and temporal through information entropy theory, and at the same time makes the information collected by the network has the lowest distortion rate. 1) Congestion mitigation in spatial. Since there is a spatial correlation between sensing data, so in the case of collecting a certain amount of data, some missing data in space can be recovered from another spatially acquired data by matrix completion theory. Therefore, in the DRCDC scheme, when congestion occurs, the amount of data transmitted (forwarded) for nodes are congested will be reduced by an intelligent approach, thereby being able to mitigate congestion. 2) Congestion mitigation in temporal. when congestion occurs, reducing the data collection of time slots containing small information to reduce the flow rate that needs to be transmitted, thereby avoiding congestion while minimizing data distortion rate of the network. The experimental results of the DRCDC scheme in a planar network show better performance than the traditional data collection schemes and reducing the packet loss 2.8%-6.8% while reduce the maximum delay by 34.2%-76.3%.

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Section: Introductionmentioning

confidence: 99%

An Efficient Information Maximization Based Adaptive Congestion Control Scheme in Wireless Sensor Network

et al. 2019

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“…Noise interferences often occur in many systems such as wireless communications [ 1 ] and social networks [ 2 , 3 ]. Hence, images are inevitably corrupted by both blur and noise during the acquisition and transmission.…”

Section: Introductionmentioning

confidence: 99%

A Convex Constraint Variational Method for Restoring Blurred Images in the Presence of Alpha-Stable Noises

Yang

Gui

2018

Sensors

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Blurred image restoration poses a great challenge under the non-Gaussian noise environments in various communication systems. In order to restore images from blur and alpha-stable noise while also preserving their edges, this paper proposes a variational method to restore the blurred images with alpha-stable noises based on the property of the meridian distribution and the total variation (TV). Since the variational model is non-convex, it cannot guarantee a global optimal solution. To overcome this drawback, we also incorporate an additional penalty term into the deblurring and denoising model and propose a strictly convex variational method. Due to the convexity of our model, the primal-dual algorithm is adopted to solve this convex variational problem. Our simulation results validate the proposed method.

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DOA Estimation Based on Logistic Function for CD Sources in Impulsive Noise

Tian,

Cai,

Luo

2024

IET Signal Processing

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To improve direction of arrival (DOA) estimation for coherently distributed sources under impulsive noise environments, a logistic‐based adaptive factor is proposed to suppress the impulsive noise contained in the output signals of the array. The properties of this adaptive factor are derived. Furthermore, this adaptive factor is applied to subspace methods, and a novel DOA estimation algorithm is proposed. This novel algorithm ensures the boundedness of the signal and the noise subspaces while improving the DOA estimation accuracy and robustness. The experimental results demonstrate that the proposed algorithm outperforms existing algorithms in terms of resolution probability and estimation accuracy under impulsive noise environments.

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Stable adaptive channel estimation method under impulsive noise environments

Cited by 3 publications

References 15 publications

An Efficient Information Maximization Based Adaptive Congestion Control Scheme in Wireless Sensor Network

An Efficient Information Maximization Based Adaptive Congestion Control Scheme in Wireless Sensor Network

A Convex Constraint Variational Method for Restoring Blurred Images in the Presence of Alpha-Stable Noises

DOA Estimation Based on Logistic Function for CD Sources in Impulsive Noise

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