Journal of Artificial Intelligence, Applications and Innovations 3060-7124 2 Journal of Artificial Intelligence, Applications and Innovations 2025 04 01 Bayesian deep learning for collaborative spectrum sensing in 6G mmWave communication systems Bayesian deep learning for collaborative spectrum sensing in 6G mmWave communication systems 74 84 EN Journal Article 2025 05 19 <p>In this paper, we present an innovative approach for spectrum sensing in mmWave massive multiple-input multiple-output (MIMO) systems by leveraging a hierarchical Bayesian model integrated with Stacked Denoising Autoencoders (SDAEs). The proposed method significantly improves the accuracy of spectrum sensing by effectively estimating the unknown covariance of the CSCG noise. By incorporating Bayesian deep learning principles into the model, a robust estimation of noise characteristics is achieved, enhancing the detection performance of weak signals amidst noise with unknown covariance. The use of Collaborative Deep Learning (CDL) allows for adaptive learning of complex noise patterns and provides a more accurate characterization of the spectrum. Extensive simulations are conducted to validate the effectiveness of the proposed CDL-based spectrum sensing method in mmWave massive MIMO systems. The simulations compare the performance of the Bayesian deep learning approach against traditional spectrum sensing techniques and state-of-the-art methods. The results demonstrate superior performance in terms of detection accuracy, noise robustness, and computational efficiency. Detailed performance metrics and analysis are provided to showcase the practical advantages of integrating CDL with SDAEs for spectrum sensing in real-world mmWave scenarios.</p> https://aiaijournal.com/index.php/aiai/article/download/37/44