A Spectrum Sensing Method Integrating DenseNet and MLP-Mixer
Along with the surge of radio application,electronic communication in interference environments has become increasingly important.The spectrum sensing technique matters in surmounting the frequency conflict of radio.However,the complex environment hinders the efficient feature extraction from the received spectrum signal and reduces the signal practicality.Recently,the artificial intelligence has been widespread in communication field and crucially influenced the electronic countermeasures.Consequently,based on the deep learning,this work proposes a spectrum sensing method to mix DenseNet and MLP-Mixer.Firstly,the model processes and transforms the spectrum signal data to feature images by Deepinsight Net and the generative adversarial networks renew an image.After obtaining the feature image,aspectrum sensing method integrating DenseNet and MLP-Mixer is used in order to sense the channel occupancy of primary user.Compared with the existing model through ablation experiments,the proposed method improves the detection probability of spectrum sensing better.
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