Spectrum map construction method based on convolutional neural network

Abstract

The invention provides a spectrum map construction method based on a convolutional neural network. The method comprises the following steps: acquiring spectrum data at different positions in a space through a sensing node; performing spatial missing value estimation on the spectrum data according to a spatial resolution requirement by adopting a Kriging interpolation method to obtain a low-resolution spectrum map image and a high-resolution spectrum map image; performing dictionary sparse construction on the low-resolution spectrum map image and the high-resolution spectrum map image, and performing image feature extraction; expressing the extracted image features through a sparse matrix to obtain a training sample set; inputting the training sample set into a preset convolutional neural network for offline training, and constructing an optimal training model by adopting a least square method; and inputting a low-resolution frequency spectrum map image into the optimal training model, and outputting a high-resolution frequency spectrum map. According to the method, the precision of the spectrum map is effectively improved while the calculation complexity is reduced.

Description

technical field [0001] The present application relates to the technical field of wireless communication, in particular to a convolutional neural network-based spectrum map construction method, device, computer equipment and storage medium. Background technique [0002] The spectrum map can visually display the spatial spectrum situation, and can be used to solve problems such as transmitter location, resource management, and interference control in wireless communications. Improving spectral map accuracy and reducing map construction costs are crucial when using spectral maps. The accuracy of the spectrum map affects the user's ability to understand the spectrum environment in the relevant environment. When constructing the spectrum map, the spectrum sensing nodes can be pre-deployed, and then the spectrum map is constructed by the spatial interpolation method. Although the deployment of more sensing nodes improves the interpolation accuracy and spatial resolution to a cer...

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Problems solved by technology

[0003] Based on this, it is necessary to provide a spectral map construction interpolation method based on a convolutional neural network to solve the problem of compromise between computational efficiency and interpolation accuracy when constructing a spectral map. At the same time, the accuracy of the spectrum map can be effectively guaranteed

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