A Study on a Spectrum Sensing Method Based on Feature-Fusion Transformer
With the rapid development of wireless communication technology,electromagnetic spectrum resources are becoming increasingly scarce,making efficient spectrum sensing and signal classification technologies crucial.Intelligent spectrum sensing and classification have become a hot research topic.This paper proposes a spectrum intelligent sensing method based on feature fusion Transformer to improve signal classification performance in complex electromagnetic environments.The proposed method designs a feature fusion layer and an improved positional encoding scheme,optimizing the Transformer architec-ture to enhance the model's ability to recognize different types of signals.Experimental results show that the improved model demonstrates significant advantages in metrics such as accuracy,achieving a classifi-cation accuracy of 99.3％,which is 4.1 percentage points higher than existing models.Furthermore,the model exhibits excellent noise resistance under different signal-to-noise ratio conditions,further proving its application potential and research value in complex electromagnetic environments.
万方数据
维普
