Citation :

Varsha Khule, Debendra Kumar Panda, "Spectrum Sensing in Cognitive Radio Networks Using Blue Whale Optimization for DTCWT and NA-EMD Feature Fusion with LDA-Based Dimensionality Reduction and AdaBoost Classification," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 52-68, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P105

Abstract

Cognitive Radio Networks (CRNs) and dynamic spectrum access offer promising approaches to improve spectrum use and address inefficiencies. These approaches are motivated by practical problems like spectrum scarcity, interference, and the increasing demands of contemporary wireless communication services. The proposed system Integrates Dual-Tree Complex Wavelet Transform (DTCWT) with the Blue Whale Optimization Algorithm (BWOA) for cognitive radio spectrum sensing. The near shift-invariance and superior resolution powers of the DTCWT help to decompose spectrum signals into multi-resolution sub-bands, which directly lead to the extraction of essential features like energy, phase, and frequency. Noise-Assisted Empirical Mode Decomposition (NA-EMD) is integrated into the framework to decompose the data streams into Intrinsic Mode Functions (IMFs), differentiating noise-dominant and signal-dominant components for adaptive, data-driven representations of the signals. Using a strong feature fusion process is a method to boost the total representation of the spectrum signal by optimally fusing statistical features obtained from DTCWT sub-bands and IMFs. Linear Discriminative Analysis (LDA) enables dimensionality reduction by selecting the most discriminative features for better classification. After this, an AdaBoost ensemble classifier is used for the classification of states of the spectrum, owing to its effectiveness in achieving an accurate performance by combining observations from a set of weak classifiers. This research resulted in a probability of detection of 0.967, indicating substantial advances over the previous approach. Additionally, it exhibited enhanced noise resilience and offered computational efficiencies while accurately adapting to dynamic feature alterations in the spectrum sensing process.

Keywords

AdaBoost, BWOA, Cognitive Radio Networks, DTCWT, IMF, LDA, Spectrum Sensing.

References

[1] N. Chitra Kiran, Cognitive Radios, 1^st ed., Towards Wireless Heterogeneity in 6G Networks, CRC Press, pp. 70-86, 2024.
[Google Scholar] [Publisher Link]

[2] Konstantinos Koufos et al., State-of-the-Art in PHY Layer Deep Learning for Future Wireless Communication Systems and Networks, 1^st ed., Deep Learning and its Applications for Vehicle Networks, CRC Press, pp. 87-114, 2023.
[Google Scholar] [Publisher Link]

[3] Qun Wang et al., “When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges,” IEEE Open Journal of the Communications Society, vol. 3, pp. 176-208, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[4] V. Devi et al., “Detection and Sensing of Cognitive Radio Spectrum using Minimum Eigen Value and TW Distribution Method,” AIP Conference Proceedings, vol. 2463, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Resmi G. Nair, and Kumar Narayanan, “Cooperative Spectrum Sensing in Cognitive Radio Networks using Machine Learning Techniques,” Applied Nanoscience, vol. 13, no. 3, pp. 2353-2363, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Amandeep Kaur, and Krishan Kumar, “A Comprehensive Survey on Machine Learning Approaches for Dynamic Spectrum Access in Cognitive Radio Networks,” Journal of Experimental Theoretical Artificial Intelligence, vol. 34, no. 1, pp. 1-40, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Srilatha Madhunala, and Bharathi Anantha, “Centralized Monitored Spectrum Management using Multi-Resource Parallel Sensing in Cognitive Radio Networks,” ACM Transactions on Sensor Networks, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Nada M. Elfatih et al., “A Double Threshold Energy Detection-based Neural Network for Cognitive Radio Networks,” Computer Systems Science and Engineering, vol. 45, no. 1, pp. 329-342, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Mohammad Hallaq, Wissam Altabban, and Omran Abbas, “Performance Analysis of the Hard-Decision Cooperative Multi-Stage Spectrum Sensing for Cognitive Radio,” International Journal of Communication Networks and Distributed Systems, vol. 29, no. 4, pp. 359-382, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[10] A. Hena Rubavathy, and S. Sundar, “A Review of Cooperative Approach for Mitigation of Fading,” AIP Conference Proceedings, vol. 2966, no. 1, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[11] L.R. Raghavendra, and R.C. Manjunatha, “Cognitive Radio Spectrum Sensing using Hybrid MME and Energy Double Thresholding Optimized with Weighted Chimp Optimization Algorithm,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 9, pp. 245-257, 2023.
[Google Scholar] [Publisher Link]

[12] Qidi Wu et al., “Differential Energy-Driven Adaptive Dual-Threshold Collaborative Spectrum Sensing Algorithm,” 2024 IEEE 7^th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, pp. 1914-1917, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Kaleem Arshid et al., “Energy Efficiency in Cognitive Radio Network using Cooperative Spectrum Sensing based on Hybrid Spectrum Handoff,” Egyptian Informatics Journal, vol. 23, no. 4, pp. 77-88, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Suresh Chinnathampy, Aruna Thangavelu, and Narayanaperumal Muthukumaran, “Performance Analysis of Efficient Spectrum Utilization in Cognitive Radio Networks by Dynamic Spectrum Access and Artificial Neuron Network Algorithms,” International Arab Journal of Information Technology, vol. 19, no. 2, pp. 224-229, 2022.
[Google Scholar] [Publisher Link]

[15] Rania A. Mokhtar, Rashid A. Saeed, and Hesham Alhumyani, “Cooperative Fusion Architecture-based Distributed Spectrum Sensing Under Rayleigh Fading Channel,” Wireless Personal Communications, vol. 124, no. 1, pp. 839-865, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Lakshmikantha Reddy Somula, and M. Meena, “K-Nearest Neighbour (KNN) Algorithm based Cooperative Spectrum Sensing in Cognitive Radio Networks,” 2022 IEEE 4^th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), Goa, India, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Duc M Cao et al., “Advanced Cybercrime Detection: A Comprehensive Study on Supervised and Unsupervised Machine Learning Approaches using Real-World Datasets,” Journal of Computer Science and Technology Studies, vol. 6, no. 1, pp. 40-48, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[18] V. Parimala, and K. Devarajan, “Modified Fuzzy C-Means and K-Means Clustering based Spectrum Sensing using Cooperative Spectrum for Cognitive Radio Networks Applications,” Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology, vol. 43, no. 3, pp. 3727-3740, 2022. [CrossRef] [Google Scholar] [Publisher Link]

[19] Liuwen Li, Wei Xie, and Xin Zhou, “Cooperative Spectrum Sensing based on LSTM-CNN Combination Network in Cognitive Radio System,” IEEE Access, vol. 11, pp. 87615-87625, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[20] S. Esakki Rajavel et al., “Optimizing Spectrum Sensing by using Artificial Neural Network in Cognitive Radio Sensor Networks,” Wireless Personal Communications, vol. 125, no. 1, pp. 803-817, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[21] M. Suba, and D. Susan, “Retracted: Performance Analysis of Cyclostationary Spectrum Sensing with Dynamic Thresholding using Artificial Neural Network under Varying Signal to Noise Ratio and Noise Variance Conditions,” Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology, vol. 45, no. 2, pp. 3247-3257, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[22] Yixuan Zhang, and Zhongqiang Luo, “A Deep-Learning-based Method for Spectrum Sensing with Multiple Feature Combination,” Electronics, vol. 13, no. 14, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[23] L.R. Raghavendra, and R.C. Manjunatha, “Optimizing Spectrum Sensing in Cognitive Radio Networks using Bayesian-Optimized Random Forest Classifier,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 6, pp. 505-518, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Wen Wu et al., “Performance Improvement for Machine Learning-based Cooperative Spectrum Sensing by Feature Vector Selection,” IET Communications, vol. 14, no. 7, pp. 1081-1089, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[25] Anilkumar Dulichand Vishwakarma, and Girish Ashok Kulkarni, “Cooperative Spectrum Sensing using Rule based Hard Decision and Soft Decision with Bayesian Optimized Support Vector Machine,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 1, pp. 405-413, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Anilkumar Dulichand Vishwakarma, and Girish Ashok Kulkarni, “Threshold Optimization in Maximum-Minimum Eigenvalue-based Detection in Cognitive Radio using Ant Colony Optimization,” Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2022, Springer, Singapore, vol. 126, pp. 855-868, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Mohammad Asif Hossain et al., “Machine Learning-based Cooperative Spectrum Sensing in Dynamic Segmentation Enabled Cognitive Radio Vehicular Network,” Energies, vol. 14, no. 4, pp. 1-30, 2021.
        [CrossRef] [Google Scholar] [Publisher Link]