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Crowd-sourcing cooperative spectrum sensing method based on data cleaning

A collaborative spectrum sensing and data purification technology, applied in the field of cognitive radio, can solve problems such as limited spectrum sensing accuracy and stability, difficulty in guaranteeing spectrum sensing data quality, and sensing data falsification

Active Publication Date: 2016-02-03
PLA UNIV OF SCI & TECH
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Problems solved by technology

However, using the spectrum sensing data obtained by popular and portable swarm intelligence wireless devices, there will be hidden dangers that the quality of spectrum sensing data cannot be guaranteed, because: (1) the spectrum sensing accuracy and stability of popular and portable swarm intelligence wireless devices (2) Open and multi-source spectrum sensing data often have security threats such as sensing data falsification

Method used

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  • Crowd-sourcing cooperative spectrum sensing method based on data cleaning
  • Crowd-sourcing cooperative spectrum sensing method based on data cleaning
  • Crowd-sourcing cooperative spectrum sensing method based on data cleaning

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comparative approach 1

[0094] Comparison scheme 1: The fusion center does not perform data purification, that is, skips step 3 of the present invention for swarm intelligence collaborative spectrum sensing data purification, and directly uses the data reported by swarm intelligence spectrum sensor nodes to perform fusion judgment according to step 4. The reference of this comparison scheme is "J.Ma, G.Zhao, and Y.Li, "Softcombinationanddetectionforcooperativespectrumsensingincognitiveradionetworks," IEEETransactionsWirelessCommunications, vol.7, no.11, pp.4502-4507, Nov.2008."

comparative approach 2

[0095] Comparison scheme 2: The fusion center changes "step 3 crowd intelligence collaborative spectrum sensing data purification" to "eliminate all nodes reporting abnormal data", and only uses the reported data of the remaining nodes for fusion judgment. Considering that it is often difficult for the fusion center to perfectly determine which nodes may report abnormal data in practice, it is assumed that the probability of the fusion center misjudging the node type is 0.1. The reference of this comparative scheme is "W.Wang, H.Li, Y.Sun, and Z.Han, "Securing collaborative spectrum sensing against trustworthy secondary users sin cognitive radionetworks," EURASIPJournalon Advances in Signal Processing, vol.2010, 2010."

comparative approach 3

[0096] Comparison scheme three: the method for spectrum sensing based on crowd intelligence collaboration based on data purification described in the present invention.

[0097] For the three comparison schemes, image 3 The relationship curve between the system detection rate and the system false alarm rate is given, through image 3 It can be seen that: given a certain system false alarm rate R false-alarm In the case of , compared with the system detection rate R of Scheme 1 detction Very low, compared with the system detection rate R of Scheme 2 detction If it is improved, the system detection rate of the method of the present invention is greatly improved. It shows that the method of the present invention can better solve the technical problems such as the ubiquity of sensing data errors and the unavoidable falsification of sensing data in popular and portable wireless spectrum sensing devices, and obtain robust collaborative spectrum sensing performance.

[0098] Spe...

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Abstract

The invention provides a crowd-sourcing cooperative spectrum sensing method based on data cleaning. The crowd-sourcing cooperative spectrum sensing method comprises the following steps: modelling crowd-sourcing cooperative spectrum sensing data; performing matriculated representation of the crowd-sourcing cooperative spectrum sensing data; cleaning the crowd-sourcing cooperative spectrum sensing data; fusing the crowd-sourcing cooperative spectrum sensing data; and evaluating a crowd-sourcing cooperative spectrum sensing performance. According to the invention, cooperative spectrum sensing can be carried out according to the crowd-sourcing cooperative spectrum sensing data obtained through portable and popular spectrum sensor equipment; and influence of data error and data fraud on the cooperative spectrum sensing performance in crowd-sourcing cooperative spectrum sensing can be eliminated.

Description

technical field [0001] The invention belongs to the cognitive radio field of wireless communication technology, and in particular relates to a data purification-based crowd intelligence collaborative spectrum sensing method. Background technique [0002] The fundamental contradiction between the explosive growth of wireless communication services and the increasingly scarce wireless spectrum resources promotes the continuous development of wireless communication technologies. As a key technology to solve this pair of basic contradictions, cognitive radio technology has received widespread attention in recent years. Its core idea is: on the premise of not affecting the normal communication of authorized users, unauthorized users can opportunistically access information not used by authorized users. Holes in the wireless spectrum. The primary challenge in implementing cognitive radio technology is how to reliably identify wireless spectrum holes. [0003] Spectrum sensing is...

Claims

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Application Information

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IPC IPC(8): H04W24/00H04B17/00
Inventor 王金龙沈良吴启晖丁国如高瞻郑学强冯烁张林元
Owner PLA UNIV OF SCI & TECH
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