Communications - Scientific Letters of the University of Zilina 2025, 27(1):E1-E10 | DOI: 10.26552/com.C.2025.003
Analysis of Hybrid Spectrum Sensing in Cognitive Radio Using Hybrid Approaches
- Department of Electronics and Communication, Oriental University, Indore, Madhya Pradesh, India
Cognitive radio (CR) technology enables dynamic spectrum access to meet the growing demand for wireless communication. This study investigates spectrum sensing methods, specifically energy detection (ED) and matched filter detection (MFD), within hybrid strategies. A novel hybrid MFD method was developed and evaluated via MATLAB simulations, analyzing factors like sample size, signal-to-noise ratio (SNR), and false alarm probability. Results reveal that ED has a higher miss-detection rate compared to MFD and the proposed hybrid method, which performs particularly well under low sample counts and SNR conditions. This research enhances spectrum sensing techniques in cognitive radio systems, paving the way for more reliable wireless communication networks.
Keywords: cognitive radio, spectrum sensing, energy detection, primary user, sensing detection
Grants and funding:
The authors received no financial support for the research, authorship and/or publication of this article.
Conflicts of interest:
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Received: May 23, 2024; Accepted: September 18, 2024; Prepublished online: October 8, 2024; Published: January 2, 2025 Show citation
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References
- SHABIR, A., KANWAL, K., AYESHA, H., ALMAS, A., RAZA, S., JAFFAR, S. A hybrid cognitive radio reporting scheme for wireless regional area networks. Journal of Computing and Biomedical Informatics [online]. 2024, Special Issue on Intelligent Computing of Applied Sciences and Emerging Trends (ICASET). ISSN 2710-1606, eISSN 2710-1614. Available from: http://jcbi.org/index.php/Main/article/view/444
- LIU, S., WU, J., HE, J. Dynamic multichannel sensing in cognitive radio: hierarchical reinforcement learning. IEEE Access [online]. 2021, 9, p. 25473-25481. eISSN 2169-3536. Available from: http://doi.org/10.1109/ACCESS.2021.3056670
Go to original source...
- SINGHAL, C., PATIL, V. HCR-WSN: Hybrid MIMO cognitive radio system for wireless sensor network. Computer Communications [online]. 202, 169, p. 11-25. ISSN 0140-3664, eISSN 1873-703X. Available from: http://doi.org/10.1016/j.comcom.2020.12.025
Go to original source...
- KHAMAYSEH, S., HALAWANI, A. Cooperative spectrum sensing in cognitive radio networks: A survey on machine learning-based methods. Journal of Telecommunications and Information Technology [online]. 2020, 3, p. 36-46. ISSN 1509-4553, eISSN 1899-8852. Available from: http://doi.org/10.26636/jtit.2020.137219
Go to original source...
- NASSER, A., CHAITOU, M., MANSOUR, A., YAO, K. C., CHARARA, H. A deep neural network model for hybrid spectrum sensing in cognitive radio. Wireless Personal Communications [online]. 2021, 118(1), p. 281-299. ISSN 0929-6212, eISSN 1572-834X. Available from: http://doi.org/10.1007/s11277-020-08013-7
Go to original source...
- MOHANAKURUP, V., BAGHELA, V. S., KUMAR, S., SRIVASTAVA, P. K., DOOHAN, N. V., SONI, M., AWAL, H. 5G cognitive radio networks using reliable hybrid deep learning based on spectrum sensing. Wireless Communications and Mobile Computing [online]. 2022, 2022, 1830497. eISSN 1530-8677. Available from: http://doi.org/10.1155/2022/1830497
Go to original source...
- ARSHID, K., JIANBIAO, Z., HUSSAIN, I., PATHAN, M. S., YAQUB, M., JAWAD, A., AHMED, F. Energy efficiency in cognitive radio network using cooperative spectrum sensing based on hybrid spectrum handoff. Egyptian Informatics Journal [online]. 2022, 23(4), p. 77-88. ISSN 1110-8665, eISSN 2090-4754. Available from: http://doi.org/10.1016/j.eij.2022.06.008
Go to original source...
- OYEWOBI, S. S., DJOUANI, K., KURIEN, A. M. A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach. Transactions on Emerging Telecommunications Technologies [online]. 2020, 31(9), e4055. ISSN 2161-3915, eISSN 2161-3915. Available from: http://doi.org/10.1002/ett.4055
Go to original source...
- HOSSAIN, M. A., NOOR, R. M., YAU, K. L. A., AZZUHRI, S. R., Z'ABA, M. R., AHMEDY, I. Comprehensive survey of machine learning approaches in cognitive radio-based vehicular ad hoc networks. IEEE Access [online]. 2020, 8, p. 78054-78108. eISSN 2169-3536. Available from: http://doi.org/10.1109/ACCESS.2020.2989870
Go to original source...
- AKTER, S., MANSOOR, N. A spectrum aware mobility pattern based routing protocol for CR-VANETs. In: 2020 IEEE Wireless Communications and Networking Conference WCNC: proceedings [online]. IEEE. 2020. eISBN 978-1-7281-3106-1, eISSN 1558-2612, p. 1-6. Available from: http://doi.org/10.1109/WCNC45663.2020.9120760
Go to original source...
- MOHAMED, A. R., EL-BANNA, A. A., MANSOUR, H. A. Multi-path hybrid spectrum sensing in cognitive radio. Arabian Journal for Science and Engineering. 2021, 46(10), p. 9377-9384. ISSN 2193-567X, eISSN 2191-4281.
Go to original source...
- MAHENDRU, G., SHUKLA, A. K., PATNAIK, L. M. An optimal and adaptive double threshold-based approach to minimize error probability for spectrum sensing at low SNR regime. Journal of Ambient Intelligence and Humanized Computing [online]. 2022, 13(8), p. 3935-3944. ISSN 1868-5137, eISSN 1868-5145. Available from: http://doi.org/10.1007/s12652-021-03596-w
Go to original source...
- FALIH, M. S., ABDULLAH, H. N. DWT based energy detection spectrum sensing method for cognitive radio system. Iraqi Journal of Information and Communication Technology [online]. 2020, 3(3), p. 1-11. ISSN 2222-758X, eISSN 2789-7362. Available from: http://doi.org/10.31987/ijict.3.3.99
Go to original source...
- USMAN, M. B., SINGH, R. S., MISHRA, S., RATHEE, D. S. Improving spectrum sensing for cognitive radio network using the energy detection with entropy method. Journal of Electrical and Computer Engineering [online]. 2022, 2022(1), 2656797. ISSN 2090-0147, eISSN 2090-0155. Available from: http://doi.org/10.1155/2022/2656797
Go to original source...
- MUSUVATHI, A. S. S., ARCHBALD, J. F., VELMURUGAN, T., SUMATHI, D., RENUGA DEVI, S., PREETHA, K. S. Efficient improvement of energy detection technique in cognitive radio networks using K-nearest neighbour (KNN) algorithm. EURASIP Journal on Wireless Communications and Networking [online]. 2024, 2024(1), 10. ISSN 1687-1472, eISSN 1687-1499. Available from: http://doi.org/10.1186/s13638-024-02338-8
Go to original source...
- SARKAR, S., MURALISHANKAR, R., GURUGOPINATH, S. Vasicek and Van Es entropy-based spectrum sensing for cognitive radios. IET Networks [online]. 2024, 13(1), p. 1-12. ISSN 2047-4954, eISSN 2047-4962. Available from: http://doi.org/10.1049/ntw2.12096
Go to original source...
- SOLANKI, S., DEHALWAR, V., CHOUDHARY, J., KOLHE, M. L., OGURA, K. Spectrum sensing in cognitive radio using CNN-RNN and Transfer learning. IEEE Access [online]. 2022, 10, p. 113482-113492. eISSN 2169-3536. Available from: http://doi.org/10.1109/ACCESS.2022.3216877
Go to original source...
- GENG, Y., HUANG, J., YANG, J., ZHANG, S. Spectrum sensing for cognitive radio based on feature extraction and deep learning. Journal of Physics: Conference Series [online]. 2022, 2261(1), 012016. ISSN 1742-6596. Available from: http://doi.org/10.1088/1742-6596/2261/1/012016
Go to original source...
- LORINCZ, J., RAMLJAK, I., BEGUSIC, D. Analysis of the impact of detection threshold adjustments and noise uncertainty on energy detection performance in MIMO-OFDM cognitive radio systems. Sensors [online]. 2022, 22(2), 631. eISSN 1424-8220. Available from: http://doi.org/10.3390/s22020631
Go to original source...
- BANI, K., KULKARNI, V. Hybrid spectrum sensing using MD and ED for cognitive radio networks. Journal of Sensor and Actuator Networks [online]. 2022, 11(3), 36. eISSN 2224-2708. Available from: http://doi.org/10.3390/jsan11030036
Go to original source...
- SHRESTHA, R., TELGOTE, S. S. (2020, October). A short sensing-time cyclostationary feature detection based spectrum sensor for cognitive radio network. In: 2020 IEEE International Symposium on Circuits and Systems ISCAS: proceedings [online]. IEEE. 2020. ISBN 978-1-7281-3320-1, eISSN 2158-1525, p. 1-5. Available from: http://doi.org/10.1109/ISCAS45731.2020.9180415
Go to original source...
- KUMAR, A., GAUR, N., CHAKRAVARTY, S., ALSHARIF, M. H., UTHANSAKUL, P., UTHANSAKUL, M. Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs. Ain Shams Engineering Journal [online]. 2024, 15(3), 102505. ISSN 2090-4479, eISSN 2090-4495. Available from: http://doi.org/10.1016/j.asej.2023.102505
Go to original source...
- VADIVELU, K., GNANAMANOHARAN, E., TAMILSELVAN, S. Detection of spectrum sensors for maximizing eigenvalue and hardware efficiency in cognitive radio networks using machine learning. International Journal of Intelligent Systems and Applications in Engineering [online]. 2024, 12(2s), p. 540-552. ISSN 2147-6799. Available from: http://ijisae.org/index.php/IJISAE/article/view/3654
- LUO, J., ZHANG, G., YAN, C. An energy detection-based spectrum-sensing method for cognitive radio. Wireless Communications and Mobile Computing [online]. 2022, 2022, 933336. eISSN 1530-8677. Available from: http://doi.org/10.1155/2022/3933336
Go to original source...
- YU, S., LIU, J., WANG, J., ULLAH, I. Adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. Wireless Communications and Mobile Computing [online]. 2020, 2020, 794136. eISSN 1530-8677. Available from: http://doi.org/10.1155/2020/4794136
Go to original source...
- SAAD, M. A., MUSTAFA, S. T., ALI, M. H., HASHIM, M. M., ISMAIL, M. B., ALI, A. H. Spectrum sensing and energy detection in cognitive networks. Indonesian Journal of Electrical Engineering and Computer Science [online]. 2020, 17(1), 464-471. ISSN 2502-4752, eISSN 2502-4760. Available from: http://doi.org/10.11591/ijeecs.v17.i1.pp464-471
Go to original source...
- MARTIAN, A., AL SAMMARRAIE, M. J. A., VLADEANU, C., POPESCU, D. C. Three-event energy detection with adaptive threshold for spectrum sensing in cognitive radio systems. Sensors [online]. 2020, 20(13), 3614. eISSN 1424-8220. Available from: http://doi.org/10.3390/s20133614
Go to original source...
- JANG, W. M. Simultaneous power harvesting and cyclostationary spectrum sensing in cognitive radios. IEEE Access [online]. 2020, 8, p. 56333-56345. eISSN 2169-3536. Available from: http://doi.org/10.1109/ACCESS.2020.2981878
Go to original source...
- TEJESH, K., BHARATHI, P. S. Effective channel detection at low SNR in cognitive radio network using matched filter approach and compare with energy detection-based approach. Revista Geintec - Gestao, Inovacao e Tecnologias. 2021, 11(2), p. 1349-1361. ISSN 2237-0722.
Go to original source...
- CHAUHAN, N., SHAH, A., BHATT, P., DALAL, P. Simulation based analysis of non-cooperative spectrum sensing techniques in cognitive radio. Test Engineering and Management. 2020, 83, p. 5149-5162. ISSN 0193-4120.
- RAGHAVENDRA, Y. M., ASHA, M., MANJULA, G., LATHA, M., SWARANALAKSHMI., HARSHITHA, R. Optimization of energy and spectrum sensing using orthogonal frequency division multiple access. International Research Journal on Advanced Engineering Hub (IRJAEH) [online]. 2024, 2(04), p. 861-869. eISSN 2584-2137. Available from: http://doi.org/10.47392/IRJAEH.2024.0121
Go to original source...
- KUMAR, A., VENKATESH, J., GAUR, N., ALSHARIF, M. H., UTHANSAKUL, P., UTHANSAKUL, M. Cyclostationary and energy detection spectrum sensing beyond 5G waveforms. Electronic Research Archive [online]. 2023, 31, p. 3400-3416. ISSN 2688-1594. Available from: http://doi.org/10.3934/era.2023172
Go to original source...
- RAI, A., SEHGAL, A., SINGAL, T. L., AGRAWAL, R. Spectrum sensing and allocation schemes for cognitive radio [online]. In: Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks. SINGH, K. K., SINGH, A., CENGIZ, K., LE, D.-N. (eds.). Scrivener Publishing LLC, 2020, ISBN 9781119640363, eISBN 9781119640554, p. 91-129. Available from: http://doi.org/10.1002/9781119640554.ch5
Go to original source...
- RAGHAVENDRA, L. R., MANJUNATHA, R. C. 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 [online]. 2023, 11(9s), p. 245-257. ISSN 2147-6799. Available from: http://ijisae.org/index.php/IJISAE/article/view/3115
- KOCKAYA, K., DEVELI, I. Spectrum sensing in cognitive radio networks: threshold optimization and analysis. EURASIP Journal on Wireless Communications and Networking [online]. 2020, 2020(1), 255. ISSN 1687-1472, eISSN 1687-1499. Available from: http://doi.org/10.1186/s13638-020-01870-7
Go to original source...
- ABED, H. S., ABDULLAH, H. N. Improvement of spectrum sensing performance in cognitive radio using modified hybrid sensing method. Acta Polytechnica [online]. 2022, 62(2), p. 228-237. ISSN 1210-2709, eISSN 1805-2363. Available from: http://doi.org/10.14311/AP.2022.62.0228
Go to original source...
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