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Method for predicting frequency spectrum of CRN (Cognitive Radio Network) on basis of GCV-RBF neural network

A cognitive radio and neural network technology, applied in electrical components, transmission monitoring, transmission systems, etc., can solve the problem of low prediction accuracy, and achieve the effect of improving prediction accuracy, good stability, and improving accuracy.

Active Publication Date: 2017-12-29
SOUTH CHINA NORMAL UNIVERSITY
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Problems solved by technology

However, traditional spectrum prediction methods generally have problems such as low prediction accuracy

Method used

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  • Method for predicting frequency spectrum of CRN (Cognitive Radio Network) on basis of GCV-RBF neural network
  • Method for predicting frequency spectrum of CRN (Cognitive Radio Network) on basis of GCV-RBF neural network
  • Method for predicting frequency spectrum of CRN (Cognitive Radio Network) on basis of GCV-RBF neural network

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Embodiment Construction

[0062] see figure 1 , which is a flowchart of a method for predicting a spectrum of a cognitive radio network based on a GCV-RBF neural network in an embodiment of the present invention, the method for predicting a spectrum of a cognitive radio network based on a GCV-RBF neural network includes the following steps:

[0063] Step 1: Obtain channel historical data information.

[0064] In one embodiment, the present invention adopts the queuing model based on M / Geo / 1 to simulate and generate channel state data as the prior data of the experiment, and selects 31100 groups of continuous sample data therefrom, wherein the 1st to 1000th groups of data are used as The training sample data is used for model training; the 1001st to 1100th sets of data are used as prediction sample data to evaluate the model prediction accuracy.

[0065] In order to further evaluate the accuracy of model prediction, in the generated channel state data, continue to take 30000 sets of sample data, and di...

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Abstract

The invention discloses a method for predicting a frequency spectrum of a CRN (Cognitive Radio Network) on the basis of a GCV-RBF neural network. The method comprises the following steps of: S1: acquiring channel historical data information; S2: using the channel historical data information as a preset input sample of an RBF neural network, training the RBF neural network by an OLS algorithm, and acquiring an optimal RBF neural network structure by a GCV evaluation method; and S3: according to the channel historical data information, by the optimal RBF neural network structure, predicting a current frequency spectrum state. Compared to the prior art, according to the invention, the optimal RBF neural network structure is obtained by the GCV evaluation method, so that the problem of overfitting in the training process is solved, and prediction accuracy is improved. Further, the RBF neural network structure, as a local approaching network, has the advantages of simple structure, high convergence rate, high real-time performance and the like, and can be sufficiently adaptive to changes of the network and improve self-adaptation of the network.

Description

technical field [0001] The invention belongs to the technical field of wireless networks, and in particular relates to a method for predicting spectrum of a cognitive radio network based on a GCV-RBF neural network. Background technique [0002] The rapid development of wireless communication technology has stimulated more and more wireless network services. As the most precious resource in wireless networks, spectrum has been difficult to meet the current and future wireless service needs. In order to solve the problem of low resource utilization caused by traditional fixed spectrum allocation schemes, Cognitive Radio (CR) technology, as an intelligent spectrum sharing technology, has attracted extensive attention from scholars at home and abroad. CR technology relies on artificial intelligence technology. The support of dynamic detection and utilization of spectrum holes fundamentally solves the problem of waste of spectrum resources caused by low spectrum utilization. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B17/391H04B17/382
CPCH04B17/382H04B17/3912
Inventor 曾碧卿胡翩翩
Owner SOUTH CHINA NORMAL UNIVERSITY
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