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Abstract

The present disclosure discloses a method for predicting a channel based on an image processing and a machine learning, which belongs to the field of the channel prediction. The method introduces an image semantic segmentation technology to identify and segment a scatterer in a scenario image, extracts the effective position information of the scatterer, and identify a scenario in the segmented image. The subsequent feature extraction is performed in a similar scenario through the scenario identification, which facilitates extracting the more tiny environment features. The semantic segmentation images of the known scenarios are jointly input into a feature extraction and channel prediction network to complete the channel prediction. Therefore, the environment information can be input more flexibly through the semantic segmentation technology, so that the accuracy of the model is improved, and the precision higher than that of a traditional channel model is finally obtained, which is beneficial for better satisfying the technical requirement of full coverage for the multi-frequency bands and multi-scenarios in a 6G system.

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