Full diversity reception based on Dempster-Shafer theory for network coding with multiple-antennas relay

Sun, Yanhua; Yi, Yang; Li, Yonghua; Zhang, Yanhua

doi:10.1109/cc.2015.7315060

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…It has become an important method in data fusion . DS theory of evidence has attracted much attention in a wide variety of fields such as intelligence, identification, automotive, fuzzy, and wireless communication . Due to the stochastic characteristics of wireless communication channels, there is uncertainty in local detection results at SUs.…”

Section: A Review Of Dempster‐shafer Evidence Theorymentioning

confidence: 99%

“…Due to the stochastic characteristics of wireless communication channels, there is uncertainty in local detection results at SUs. Considering that DS theory is used in managing uncertainty, it is a good choice for decision making in CR systems . In this section, a brief review of DS theory of evidence is carried out.…”

Section: A Review Of Dempster‐shafer Evidence Theorymentioning

confidence: 99%

“…In Reference , a method was proposed for combining all SUs spectrum decisions and their self‐assessed credibility of each decision by means of Dempster‐Shafer theory (DS theory) of evidence, which is suitable for fast‐changing radio frequency (RF) environments, due to its ability to assign uncertainty to propositions. However, under practical conditions, illogical results may be obtained by the DS theory combination rule when the conflicts between SUs sensing data are high, leading to low performance. In Reference , a method was proposed to try to overcome this problem by assigning a relative relationship between SUs to adjust the credibility of the decision.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the combination of conflicting evidences with the classical DS theory rule may produce counter‐intuitive results when combining the SUs sensing data evidence leading to poor CSS performance. Hence, in order to minimize the effect of conflicting data along the trend of research in References , a novel efficient evidence‐based decision fusion scheme CSS is proposed in this article. This approach is based on the credibility of evidence from the SUs, which represents the similarity or the relation among different SUs sensing data evidence, and a dissociability degree measure that indicates the quality or clarity of the SUs sensing data evidence.…”

Section: Introductionmentioning

confidence: 99%

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Efficient evidence‐based decision fusion scheme for cooperative spectrum sensing in cognitive radio networks

Simpson

Sun

2020

Trans Emerging Tel Tech

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In this article, an evidence based decision fusion cooperative spectrum sensing (CSS) schemes has been considered for overcoming the hidden terminal problem, improving reliability, and increasing SU agility. Under practical conditions, the combination of conflicting evidences with the classical Dempster-Shafer theory (DS theory) rule may produce counter-intuitive results when combining the secondary users (SUs) sensing data evidence leading to poor CSS performance. In order to overcome and minimize the effect of conflicting data, and to enhance performance of the CSS system, a novel efficient evidence-based decision fusion scheme CSS is proposed. The approach is based on the credibility of evidence from the SUs sensing decision, which represents the similarity or the relation among the different SUs sensing data evidence, and a dissociability degree measure that indicates the quality or clarity of the SUs sensing data evidence. Furthermore, a weighted averaging factor determined by the credibility and dissociability of the SU sensing data evidence is proposed. Simulation results presented show that under practical conditions the proposed scheme enhances the performance of the CSS system when compared to traditional fusion rules that do not take into account the difference in local sensing reliability between the SUs.

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Section: A Review Of Dempster‐shafer Evidence Theorymentioning

confidence: 99%

Section: A Review Of Dempster‐shafer Evidence Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient evidence‐based decision fusion scheme for cooperative spectrum sensing in cognitive radio networks

Simpson

Sun

2020

Trans Emerging Tel Tech

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“…In rough set theory, knowledge is regarded as the division of the universal, that is, knowledge is considered to be granular, and the uncertainty is caused by the large granularity in the knowledge. Different from the DS evidence theory [39] and the fuzzy set theory [14,24], the membership function value of the object in the rough set theory depends on the knowledge base. It can be directly obtained from the required data without any prior knowledge or additional information.…”

Section: Remote Sensing Image Feature Model Based On Rough Setmentioning

confidence: 99%

A multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition

Huang

Chen

Wang

2019

Multimed Tools Appl

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The effective extraction of continuous features in ocean optical remote sensing image is the key to achieve the automatic detection and identification for marine vessel targets. Since many of the existing data mining algorithms can only deal with discrete attributes, it is necessary to transform the continuous features into discrete ones for adapting to these intelligent algorithms. However, most of the current discretization methods do not consider the mutual exclusion within the attribute set when selecting breakpoints, and cannot guarantee that the indiscernible relationship of information system is not destroyed. Obviously, they are not suitable for processing ocean optical remote sensing data with multiple features. Aiming at this problem, a multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition is presented in this paper. Firstly, the information equivalent model of remote sensing image is established based on the theories of information entropy and rough set. Secondly, the change extent of indiscernible relationship in the model before and after discretization is evaluated. Thirdly, multiple scans are executed for each band until the termination condition is satisfied for generating the optimal number of intervals. Finally, we carry out the simulation analysis of the high-resolution remote sensing image data collected near the coast of South China Sea. In addition, we also compare the proposed method with the current mainstream discretization algorithms. Experiments validate that the proposed method has better comprehensive performance in terms of interval number, data consistency, running time, prediction accuracy and recognition rate.

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Adaptive correlation techniques for spread spectrum communication systems

Michaels

Chester

2016

MILCOM 2016 - 2016 IEEE Military Communications Conference

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Full diversity reception based on Dempster-Shafer theory for network coding with multiple-antennas relay

Cited by 7 publications

References 19 publications

Efficient evidence‐based decision fusion scheme for cooperative spectrum sensing in cognitive radio networks

Efficient evidence‐based decision fusion scheme for cooperative spectrum sensing in cognitive radio networks

A multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition

Adaptive correlation techniques for spread spectrum communication systems

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