Bayesian deep learning for collaborative spectrum sensing in 6G mmWave communication systems
Keywords:
Cognitive radio network, Spectrum sensing, Baysian deep learning, Massive MIMOAbstract
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.
Downloads
References
M. Nouri, H. Behroozi, N. K. Mallat, and S. A. Aghdam, "A wideband 5G cyclostationary spectrum sensing method by kernel least mean square algorithm for cognitive radio networks," IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 7, pp. 2700-2704, 2021, doi: 10.1109/TCSII.2021.3051087.
E. K. Fard, M. Nouri, H. Behroozi, and S. Sobhi-Givi, "Multi-Agent H-Learning Based Cooperative Spectrum Sensing for Cognitive Radio Networks," in 32nd International Conference on Electrical Engineering (ICEE), 2024, pp. 1-5, doi: 10.1109/ICEE63041.2024.10668166.
M. Nouri et al., "Hybrid precoding based on active learning for mmWave massive MIMO communication systems," IEEE Transactions on Communications, vol. 71, no. 5, pp. 3043-3058, 2023, doi: 10.1109/TCOMM.2023.3247768.
S. Sobhi-Givi, M. Nouri, M. G. Shayesteh, H. Kalbkhani, and Z. Ding, "Reinforcement Learning Based Joint Resource Allocation and User Fairness Optimization in mmWave-NOMA HetNets," in 2023 31st International Conference on Electrical Engineering (ICEE), 2023, pp. 781-786, doi: 10.1109/ICEE59167.2023.10334709.
O. P. Awe, A. Deligiannis, and S. Lambotharan, "Spatio-temporal spectrum sensing in cognitive radio networks using beamformer-aided SVM algorithms," IEEE Access, vol. 6, pp. 25377-25388, 2018, doi: 10.1109/ACCESS.2018.2825603.
D. Wang and Z. Yang, "An advanced scheme with decision tree for the improvement of spectrum sensing efficiency in dynamic network," in 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2016, pp. 1288-1292, doi: 10.1109/CISP-BMEI.2016.7852914.
M. Nouri, S. Sobhi-Givi, H. Behroozi, M. G. Shayesteh, M. J. Piran, and Z. Ding, "Joint Slice Resource Allocation and Hybrid Beamforming with Deep Reinforcement Learning for NOMA based Vehicular 6G Communications," IEEE Transactions on Network and Service Management, 2025, doi: 10.1109/TNSM.2025.3561251.
M. Rahimi, S. Sobhi-Givi, M. Nouri, H. Behroozi, and M. Uysal, "A Deep Reinforcement Learning Framework for Joint Bs and Beyond Diagonal Ris Beamforming Design in Isac Systems," in 2025 13th Iran Workshop on Communication and Information Theory (IWCIT), 2025, pp. 1-6, doi: 10.1109/IWCIT67595.2025.11073027.
S. Sobhi-Givi, M. Nouri, H. Behroozi, and Z. Ding, "Joint BS and Beyond Diagonal RIS Beamforming Design with DRL Methods for mmWave 6G Mobile Communications," in 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024, pp. 1-6, doi: 10.1109/WCNC57260.2024.10571253.
C. J. Swinney and J. C. Woods, "K-means clustering approach to UAS classification via graphical signal representation of radio frequency signals for air traffic early warning," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 24957-24965, 2022, doi: 10.1109/TITS.2022.3202011.
F. Pourkamali-Anaraki and S. Becker, "Preconditioned data sparsification for big data with applications to PCA and K-means," IEEE Transactions on Information Theory, vol. 63, no. 5, pp. 2954-2974, 2017, doi: 10.1109/TIT.2017.2672725.
S. R. Shebert, A. F. Martone, and R. M. Buehrer, "Wireless standard classification using convolutional neural networks," in 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685799.
L. Wang, J. Hu, D. Jiang, C. Zhang, R. Jiang, and Z. Chen, "Deep Learning Models for Spectrum Prediction: A Review," IEEE Sensors Journal, 2024, doi: 10.1109/JSEN.2024.3416738.
W. Lee, M. Kim, and D. H. Cho, "Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks," IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 3005-3009, 2019, doi: 10.1109/TVT.2019.2891291.
L. Xu et al., "Spectrum Prediction for Mobile Internet of Things Based on a DB-LSTM Algorithm," IEEE Transactions on Vehicular Technology, 2024, doi: 10.1109/TVT.2024.3401144.
K. Davaslioglu and Y. E. Sagduyu, "Generative adversarial learning for spectrum sensing," in 2018 IEEE international conference on communications (ICC), 2018, pp. 1-6, doi: 10.1109/ICC.2018.8422223.
R. Zhao, Y. Ruan, Y. Li, T. Li, and R. Zhang, "CCD-GAN for Domain Adaptation in Time-frequency Localization based Wideband Spectrum Sensing," IEEE Communications Letters, 2023, doi: 10.1109/LCOMM.2023.3298434.
Y. Fu and Z. He, "Bayesian-inference-based sliding window trust model against probabilistic SSDF attack in cognitive radio networks," IEEE Systems Journal, vol. 14, no. 2, pp. 1764-1775, 2019, doi: 10.1109/JSYST.2019.2936263.
M. Nouri, M. G. Shayesteh, H. Behroozi, and Z. Ding, "Digital/Hybrid Beamforming via KLMS Algorithm in Presence of Mutual Coupling with Experimental Evaluation for 5G and V2V Communications," IEEE Transactions on Vehicular Technology, 2023, doi: 10.1109/TVT.2023.3330747.
W. Hao and D. Y. Yeung, "Towards Bayesian deep learning: a framework and some existing methods," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 12, pp. 3395-3408, 2016, doi: 10.1109/TKDE.2016.2606428.
E. K. Fard, M. Nouri, H. Behroozi, and S. Sobhi-Givi, "Multi-Agent H-Learning Based Cooperative Spectrum Sensing for Cognitive Radio Networks," in 2024 32nd International Conference on Electrical Engineering (ICEE), 2024, pp. 1-5, doi: 10.1109/ICEE63041.2024.10668166.
S. Sobhi-Givi, M. Nouri, M. G. Shayesteh, H. Behroozi, H. H. Kwon, and M. J. Piran, "Efficient Optimization in RIS-Assisted UAV System Using Deep Reinforcement Learning for mmWave-NOMA 6G Communications," IEEE Internet of Things Journal, 2025, doi: 10.1109/JIOT.2025.3553176.
M. Rahimi, M. Nouri, S. Sobhi-Givi, H. Behroozi, M. J. Piran, and N. K. Mallat, "GAI-Driven Semantic CSI Quantization for Aerial BD-RIS-Assisted NOMA in Future Consumer Networks," IEEE Transactions on Consumer Electronics, 2026, doi: 10.1109/TCE.2026.3661397.
M. Khan, D. Nielsen, V. Tangkaratt, W. Lin, Y. Gal, and A. Srivastava, "Fast and scalable bayesian deep learning by weight-perturbation in adam," in International conference on machine learning, 2018, pp. 2611-2620. [Online]. Available: https://proceedings.mlr.press/v80/khan18a.html.
S. Sobhi-Givi, M. G. Shayesteh, and H. Kalbkhani, "Energy-efficient power allocation and user selection for mmWave-NOMA transmission in M2M communications underlaying cellular heterogeneous networks," IEEE Transactions on Vehicular Technology, vol. 69, no. 9, pp. 9866-9881, 2020, doi: 10.1109/TVT.2020.3003062.
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 Mahdi Nouri, Elaheh Karimpour Fard, Sima Sobhi-Givi (Author); Hamid Behroozi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
