Signal spectral hole perception method based on temporal attention mechanism and lstm model

Abstract

The invention provides a signal spectrum hole sensing method based on a timing attention mechanism and an LSTM model, and relates to the technical field of wireless communication. Including the following steps: Step 1: Receive spectral data and binarize it; Step 2: Serialize the spectral data and construct a spectral data set; Step 3: Build and train an LSTM model, and input the serialized spectral data set In the model; step 4: use the model to extract the time series features in the spectral data, the time series features include multiple hidden state features of each group of time series numbers h t ; Step 5: Use the time series attention mechanism in the model to learn the time series features, obtain the prediction vector of the model, and obtain the user's signal state by judging the median value of the prediction vector. The invention uses the attention mechanism in the spectrum hole perception task, improves the prediction performance when the spectrum signal distribution is complex, and significantly improves the accuracy of spectrum hole perception.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a signal spectrum hole sensing method based on a timing attention mechanism and an LSTM model. Background technique [0002] The spectral hole sensing method is one of the important tasks of cognitive radio, which affects all aspects of people's lives, ranging from facilitating communication between people to promoting the development of the country's social economy. However, with the continuous increase of the number of users, new challenges are brought to the wireless communication technology, and the contradiction between user needs and insufficient spectrum resources becomes increasingly prominent. According to the research conducted by the University of California, Berkely, on the measurement of the 30MHz to 6GHZ frequency band, it is found that the current utilization efficiency of the spectrum in the time domain and frequency domain is less than 50%. In ord...

AI Technical Summary

Problems solved by technology

Using the LSTM model based on the time series attention mechanism to fully extract the time series features of the spectrum data, without prior information of the signal, to solve the problem that the traditional spectrum hole perception method has poor perception effect under low signal-to-noise ratio, requires prior information, and is interfered by adjacent frequency bands And other issues

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