Frequency spectrum prediction sensing method based on RCS-GRU model

Abstract

The invention belongs to the technical field of cognitive radio, and particularly relates to a frequency spectrum prediction sensing method based on an RCS-GRU model. The method comprises the following steps: simulating a channel by adopting an M/M/N queuing theory model; combining and splicing the channel state sequences simulated in the step 1 into a matrix; establishing a convolutional neural network model based on residual CBAM, inputting the spliced channel state matrix set into the model, and extracting features of spectrum occupancy states among channels; inputting the extracted feature data set into a GRU model, mining the features of the channel state in the time sequence, performing spectrum prediction, and outputting the channel state of the next time slot; adopting an Adam optimization algorithm to set a variable learning rate optimization cross loss function to train an RCS-GRU network, and adding a dropout method in the training process; and evaluating the prediction performance of the RCS-GRU by adopting a relation curve of the false alarm prediction probability and the detection prediction probability and a root mean square error (RMSE).

Description

technical field [0001] The present invention belongs to the field of cognitive radio technology, and more specifically, relates to a spectrum prediction sensing method based on an RCS-GRU model. Background technique [0002] Cognitive Radio Networks (CRNs) can effectively solve the contradiction between the shortage of wireless spectrum resources and the low utilization rate of wireless spectrum through dynamic spectrum access DSA (Dynamic spectrum access) and spectrum resource management technology, and improve the communication of the system. capacity. [0003] However, the CRNs system faces many technical challenges, one of which is the spectrum sensing technology. Spectrum sensing technology enables cognitive users to use effective signal detection or sensing methods to obtain spectrum resource usage in authorized wireless communication systems. However, in traditional spectrum sensing, cognitive users often cause huge processing delay and energy consumption when scann...

AI Technical Summary

Problems solved by technology

[0005] The above prediction-based spectrum sensing methods still have the following problems: (1) The correlation of spectrum occupancy between multiple channels is not considered, and the detailed features cannot be fully captured in the process of extracting spectrum occupancy status features; (2) The time consumed in prediction is too long Long, the prediction accuracy is not enough

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