Optimizing Cognitive Radio Networks with Deep Learning-Based Semantic Spectrum Sensing
DOI:
https://doi.org/10.26636/jtit.2024.4.1797Keywords:
cognitive radio, ResNet-50, sand cat optimizer, semantic spectrum sensing, wireless sensor networkAbstract
Spectrum aggregation in 4G and 5G networks is a technique used to combine multiple frequency bands to boost communication performance. The cognitive radio feature improves the ability to combine spectrum in LTE and 5G environments by enabling dynamic spectrum sensing. Spectrum sensing is a major problem in spectrum aggregation due to the presence of various types of interference, such as noise. Phase noise is an issue due to its 1 MHz frequency offset experienced within 5G's 28 GHz operating band, with the distorted signal generating more spectrum sensing-related errors. To solve this problem, the proposed work suggests an optimized deep learning-based semantic spectrum sensing model using three sets of optimizers (ResNet-50, DeepLab V3 and sand cat) offering a high detection accuracy of 99.7% with the optimized training parameter of a high signal-to-noise ratio equaling 40 dB.
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[1] P.H. Talajiya, A. Gangurde, U. Ragavendran, and H. Murali, "Cognitive Radio Networks and Spectrum Sensing: A Review", International Journal of Online and Biomedical Engineering, vol. 16, no. 13, pp. 4-18, 2020.
View in Google Scholar
[2] J. Xie, J. Fang, C. Liu, and L. Yang, "Unsupervised Deep Spectrum Sensing: A Variational Auto-encoder Based Approach", IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 5307-5319, 2020.
View in Google Scholar
[3] R. Fan, Q. Gu, X. An, and Y. Zhang, "Robust Power Allocation for Cognitive Radio System in Underlay Mode", in: IoT as a Service, pp. 426-430, 2018.
View in Google Scholar
[4] Z. Zhang, "Research on the Performance of Overlay/Underlay Cognitive Radio Waveforms in Different Channels", 9th Int. Symposium on Next Generation Electronics (ISNE), Changsha, China, 2021.
View in Google Scholar
[5] X. Lu et al., "Integrated Use of Licensed- and Unlicensed-Band mmWave Radio Technology in 5G and Beyond", IEEE Access, vol. 7, pp. 24376-24391, 2019.
View in Google Scholar
[6] G. Eappen, T. Shankar, and R. Nilavalan, "Cooperative Relay Spectrum Sensing for Cognitive Radio Network: Mutated MWOA-SNN Approach", Applied Soft Computing, vol. 114, art. no. 108072, 2022.
View in Google Scholar
[7] Z. Gu et al., "Efficient Distributed Broadcasting Algorithms for Cognitive Radio Networks-enabled Smart Agriculture", Computers and Electrical Engineering, vol.108, art. no. 108690, 2023.
View in Google Scholar
[8] T. Sheng et al., "Dynamic Spectrum Sharing and Aggregation Scheme Based on Deep Reinforcement Learning", International Wireless Communications and Mobile Computing, Dubrovnik, Croatia, 2022.
View in Google Scholar
[9] R. Ahmed, Y. Chen, and B. Hassan, "Optimal Spectrum Sensing in MIMO-based Cognitive Radio Wireless Sensor Network (CR-WSN) Using GLRT with Noise Uncertainty at Low SNR", International Journal of Electronics and Communications, vol. 136, art. no. 153741, 2021.
View in Google Scholar
[10] A. Haldorai and A. Ramu, "Security and Channel Noise Management in Cognitive Radio Networks", Computers & Electrical Engineering, vol. 87, art. no. 106784, 2020.
View in Google Scholar
[11] W. Xu, H. Lai, J. Dai, and Y. Zhou, "Spectrum Sensing for Cognitive Radio Based on Kendall’s Tau in the Presence of Non-Gaussian Impulsive Noise", Digital Signal Processing, vol. 123, art. no. 103443, 2022.
View in Google Scholar
[12] C. Charan and R. Pandey, "Eigenvalue-based Spectrum Sensing under Correlated Noise for Multi-dimensional Cognitive Radio Receiver", Wireless Personal Communication, vol. 133, pp. 227-244, 2023.
View in Google Scholar
[13] M. Girmay et al., "Enabling Uncoordinated Dynamic Spectrum Sharing between LTE and NR Networks", IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 5953-5968, 2024.
View in Google Scholar
[14] S. Kim, "4G/5G Coexistent Dynamic Spectrum Sharing Scheme Based on Dual Bargaining Game Approach", Computer Communications, vol. 181, pp. 215-223, 2022.
View in Google Scholar
[15] F. Ali and H. Yigang, "Spectrum Sensing-focused Cognitive Radio Network for 5G Revolution", Frontiers in Environmental Science, vol. 11, 2023.
View in Google Scholar
[16] D.S. Sofia and A.S. Edward, "Overlay Dynamic Spectrum Sharing in Cognitive Radio for 4G and 5G Using FBMC", Materials Today: Proceedings, vol. 80, pp. 2781-2785, 2023.
View in Google Scholar
[17] M. Wasilewska, H. Bogucka, and A. Kliks, "Spectrum Sensing and Prediction for 5G Radio", in: Big Data Technologies and Application, vol. 371, pp. 176-194, 2021.
View in Google Scholar
[18] S. Zheng et al., "Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios", China Communications, vol. 17, no. 2, pp. 138-148, 2020.
View in Google Scholar
[19] Y. Geng, J. Huang, J. Yang, and S. Zhang, "Spectrum Sensing for Cognitive Radio Based on Feature Extraction and Deep Learning", Journal of Physics: Conference Series, vol. 2261, 2022.
View in Google Scholar
[20] Z. Sabrina et al., "Spectrum Sensing Based on an Improved Deep Learning Classification for Cognitive Radio", International Conference on Electrical, Computer, Communications and Mechatronics Engineering, Male, Maldives, 2022.
View in Google Scholar
[21] H. Xing et al., "Spectrum Sensing in Cognitive Radio: A Deep Learning Based Model", Transactions on Emerging Telecommunications Technologies, vol. 33, no. 1, 2022.
View in Google Scholar
[22] Y. Mishra and V.S. Chaudhary, "Spectrum Sensing in Cognitive Radio for Internet of Things using Deep Learning Models", SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, vol. 15, no. 1, pp. 27-33, 2023.
View in Google Scholar
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Copyright (c) 2024 Mahesh Kumar N, Arthi R
This work is licensed under a Creative Commons Attribution 4.0 International License.
