Spectrum sensing method and device based on machine learning in noma system

Abstract

The present application relates to a machine learning-based spectrum sensing method, device, computer equipment and storage medium in the NOMA system. The method includes: when it is necessary to use the target frequency band to send a message, sending a sensing application of the target frequency band to the corresponding base station, so that the base station issues a spectrum sensing task for the target frequency band to each user within the coverage area; Spectrum observation energy signals collected by secondary users in the sensing time slot; according to the location information of each user, the spectrum observation energy values ​​corresponding to each spectrum observation energy signal are filled into the spectrum energy observation matrix, and the obtained energy observation matrix is ​​pre-formed. Processing, the processed energy observation matrix is ​​converted into an image, and the obtained grayscale image is input to the spectrum sensing model based on the Shuffle-Dense neural network for identification, and the current channel state of the target frequency band is output, which improves the accuracy of spectrum sensing.

Description

technical field [0001] The present application relates to the technical field of wireless communication, and in particular to a machine learning-based spectrum sensing method, device, computer equipment and storage medium in the NOMA system. Background technique [0002] With the emergence of emerging applications such as autonomous driving, telemedicine, and augmented reality, as well as the rapid increase in the number of smart terminals such as smartphones and drones, traditional mobile communication networks have been unable to meet the business needs of contemporary users. The requirements for speed, low latency, and massive access are getting higher and higher. The data rate demand of 5G networks is increasing exponentially, and a large amount of spectrum resources are urgently needed. NOMA (Non Orthogonal Multiple Access, non-orthogonal multiple access) technology further improves the system capacity and improves the utilization rate of spectrum resources through pow...

AI Technical Summary

Problems solved by technology

[0004] Existing schemes provide an exhaustive mathematical description of CSS under NOMA in the power domain, and analyze the performance of unsupervised learning algorithms (K-Means and GMM) and supervised learning algorithms (DAG-SVM, KNN, and BP neural network) under NOMA. performance on the CSS problem, but the accuracy of existing spectrum sensing is low

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