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Spectrum sensing method, system and medium based on random forest algorithm

A random forest algorithm and spectrum sensing technology, applied in the transmission system, transmission monitoring, advanced technology, etc., can solve the problems that cannot meet the diverse development needs of the communication field, achieve accurate spectrum sensing, and reduce time costs

Active Publication Date: 2022-07-12
EAST CHINA INST OF COMPUTING TECH
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  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

However, with the advancement of software radio, simply sensing the existence of the primary user can no longer meet the diverse development needs in the communication field, and it is necessary to be able to perceive more electromagnetic information, such as signal parameters of received signals or space electromagnetic environment parameters

Method used

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  • Spectrum sensing method, system and medium based on random forest algorithm
  • Spectrum sensing method, system and medium based on random forest algorithm
  • Spectrum sensing method, system and medium based on random forest algorithm

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Embodiment

[0051] The present invention studies a spectrum sensing algorithm based on random forest. The algorithm uses the high-order cumulant to form the characteristics of the sensing signal, as the basis for judging the existence of the main user and the modulation mode of the main user signal, and uses the C4.5 algorithm to train to form a random signal. The forest classifier overcomes the overfitting problem caused by the single classifier of the decision tree algorithm.

[0052] Specific implementation steps:

[0053] Step 1: Convert the signal training data set into a high-order cumulant feature sample set according to the high-order cumulant calculation formula.

[0054] The formula for calculating the higher-order cumulant is as follows:

[0055] C 20 =cum(s[n],s[n])=M 20

[0056] C 21 =cum(s[n],s * [n])=M 21

[0057]

[0058] C 41 =cum(s[n],s[n],s[n],s * [n])=M 41 -3M 20 M 21

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] The ch...

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Abstract

The present invention provides a spectrum sensing method, system and medium based on random forest algorithm, including: step 1: receiving a signal through an antenna, converting a signal training data set into a feature sample set according to a high-order cumulant calculation formula; step 2 : Construct a decision tree, filter the feature sample set to form a sub-sample set and a sub-feature set; Step 3: Calculate the average value of each feature in the sub-feature set in the sub-sample set; Step 4: Calculate the information gain rate based on the features and the average value , the feature with the largest information gain rate is used as the split node; Step 5: Divide the sub-sample set according to the split node, and build a decision tree; Step 6: Build a random forest according to the decision tree; Step 7: Use the sample set to be tested to test the random forest , get the modulation type of the test sample signal. The algorithm of the invention trains the signal classifier in an offline manner, and integrates it into the radio frequency front-end module of the cognitive radio system, thereby reducing the time cost of the radio system.

Description

technical field [0001] The present invention relates to the technical field of spectrum sensing, and in particular, to a spectrum sensing method, system and medium based on a random forest algorithm. Background technique [0002] Decision tree: Decision tree is based on the known probability of occurrence of various situations, by forming a decision tree to find the probability that the expected value of the net present value is greater than or equal to zero, evaluate the project risk, and judge its feasibility. The decision analysis method is intuitive. A graphical method using probabilistic analysis. Decision tree is a very common classification method. It is a kind of supervised learning. The so-called supervised learning is given a bunch of samples, each sample has a set of attributes and a category, these categories are determined in advance, then a classifier is obtained through learning, and this classifier can The object is given the correct classification. [000...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B17/382H04B17/391
CPCH04B17/382H04B17/391Y02D30/70
Inventor 张靖雯江波赵华徐悦
Owner EAST CHINA INST OF COMPUTING TECH
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