Cooperative Spectrum Sensing With DeGAN and KANCNN for Nonorthogonal Multiple Access

Non-Orthogonal Multiple Access (NOMA) technology achieves higher communication throughput compared to Orthogonal Multiple Access, but it also introduces significant challenges for spectrum sensing, particularly in accurately detecting channels occupied by multiple users in complex environments. To address these challenges and enhance spectrum sensing capabilities in power-domain NOMA, this paper proposes a novel deep learning-based algorithm that integrates a Denoising Generative Adversarial Network (DeGAN) for effective noise reduction. The DeGAN module employs a joint loss function to effectively suppress noise while preserving critical time-frequency features of the signals. Subsequently, the power spectral density of the denoised signals is extracted and utilized as a feature for sample classification through a model that integrates the Kolmogorov-Arnold Network and Convolutional Neural Network (KANCNN). Comparative results demonstrate that the DeGAN-KANCNN algorithm surpasses other methods in detection accuracy and interference resistance in challenging environments.