A Generic Composite Hypothesis Using Horizontal and Vertical Frameworks for AWGN Blind Detection in a Cognitive Radio System

Zakka Augustine; Agbotiname Lucky Imoize; Samson Isaac; Thomas Lass  Barma; John Joshua

doi:10.64509/jicn.22.114

Authors

Zakka Augustine Department of Computer Sciences, Faculty of Computing, Federal University of Applied Sciences, Kachia 802101, Nigeria. Author https://orcid.org/0000-0002-7366-7071
Agbotiname Lucky Imoize Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos 100213, Nigeria. Author https://orcid.org/0000-0001-8921-8353
Samson Isaac Department of Computer Science, Faculty of Computing, Federal University of Applied Sciences, Kachia 802101, Nigeria. Author https://orcid.org/0000-0002-8282-3961
Thomas Lass Barma Department of Software Engineering, Faculty of Computing, Federal University of Applied Sciences, Kachia 802101, Nigeria. Author https://orcid.org/0000-0003-0236-6463
John Joshua Department of Computer Science, Faculty of Computing, Federal University of Applied Sciences, Kachia 802101, Nigeria. Author https://orcid.org/0009-0006-4473-7751

DOI:

https://doi.org/10.64509/jicn.22.114

Keywords:

Binary Hypothesis, Composite Hypothesis, Horizontal Hypothesis, Vertical Hypothesis, Spectrum Sensing

Abstract

Classifying the Additive White Gaussian Noise (AWGN) and the Primary User (PU) transmission signal has been a crucial area of research interest. For effective data transmission, the Secondary User (SU) detection threshold needs to be continuously adjusted to distinguish between the AWGN and PU signals at a very low Signal-to-Noise Ratio (SNR). The research analytically compared a proposed Vertical Hypothesis (VH) against the Horizontal Hypothesis (HH) of Spectrum Sensing (SS) within a Cognitive Radio (CR) framework. The developed model aims to increase SU sensitivity and specificity to AWGN and PU signal powers. The performance indices used are the composite hypotheses of spectrum detection P_d, missed detection P_md, to obtain a false alarm P_fa, and blind detection, P_bd. The results obtained clearly showed that the vertical approach, when varied, achieved a threshold of about 0.96 blind detection and 0.04 false alarm at -10 dB channel gain between PU and SU, which is an improvement over the IEEE guideline threshold of 0.9 spectrum detection and 0.1 false alarm, respectively. The vertical approach proved more efficient at optimizing radio spectrum utilization, potentially improving transmission quality in Fifth Generation (5G) and Sixth Generation (6G) wireless systems.

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References

[1] Devasis, P., Priyanka, K.C.: Effectiveness of spectrum sensing in cognitive radio toward 5G technology. Saudi Journal of Engineering and Technology 4(12), 473-785 (2019). https://doi.org/10.36348/sjeat.2019.v04i12.001 DOI: https://doi.org/10.36348/sjeat.2019.v04i12.001

[2] Vaduganathan, L., Neware, S., Falkowski-Gilski, P., Divakarachari, P.B.: Spectrum sensing based on hybrid spectrum handoff in cognitive radio networks. Entropy 25(9), 1285 (2023). https://doi.org/10.3390/e25091285 DOI: https://doi.org/10.3390/e25091285

[3] Khamayesh, S., Halawani, A.: Cooperative Spectrum Sensing in Cognitive Radio Networks: A Survey on Machine Learning-based Methods. Journal of Telecommunications and Information Technology 81(3), 36-46 (2020). https://doi.org/10.26636/jtit.2020.137219 DOI: https://doi.org/10.26636/jtit.2020.137219

[4] Ezhilarasi, E., Clement, J.C.: Robust Cooperative spectrum sensing in Cognitive Radio Blockchain network using SHA-3 algorithm. Blockchain: Research and Applications 5(4), 100224 (2024). https://doi.org/10.1016/j.bcra.2024.100224 DOI: https://doi.org/10.1016/j.bcra.2024.100224

[5] Arjoune, Y., Kaabouch, N.: A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions. Sensors 19(1), 126 (2019). https://doi.org/10.3390/s19010126 DOI: https://doi.org/10.3390/s19010126

[6] Abilasha, V., Karthikeyan, A.: Integrated energy optimization and emulation attack mitigation technique for CRSN under Rayleigh fading channel. Scientific Reports 16, 1249 (2026). https://doi.org/10.1038/s41598-025-30933-2 DOI: https://doi.org/10.1038/s41598-025-30933-2

[7] Raji, A.A., Olwal, T.O.: Spectrum Sensing in Cognitive Radio Internet of Things: State-of-the-Art, Applications, Challenges, and Future Prospects. Journal of Sensor and Actuator Networks 14(6), 109 (2025). https://doi.org/10.3390/jsan14060109 DOI: https://doi.org/10.3390/jsan14060109

[8] Aswatha, R., Seethalakshmi, V., Murugan, K., Sathishkumar, N., Reethika, A., Gnanandhini, S.: Implementation of cooperative spectrum sensing using cognitive radio testbed. Indian Journal of Science and Technology 13(13), 1355-1366 (2020). https://doi.org/10.17485/IJST/v13i13.94 DOI: https://doi.org/10.17485/IJST/v13i13.94

[9] Islam, S., Budati, A.K., Hasan, M.K., Mahfoudh, S., Shah, S.B.H.: Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks. Computer Modeling in Engineering & Sciences (CMES) 137(1), 813-825 (2023). http://dx.doi.org/10.32604/cmes.2023.027595 DOI: https://doi.org/10.32604/cmes.2023.027595

[10] Augustine, Z., Alabi, C., Njoku, F.C., Imoize, A.L., Raymond, J.: Throughput Optimization Using Spectrum Sensing Vertical Hypotheses Integrated With FOFDM Technique. Journal of Intelligent Computing and Networking 2(1), 1-12 (2026). https://doi.org/10.64509/jicn.21.50 DOI: https://doi.org/10.64509/jicn.21.50

[11] Subekti, A., Rachmana, S.N., Sugihartono, Suksmon, B.A.: A Blind Spectrum Sensing for Cognitive Radio Based on Jarque-Bera Normality Test. International Journal on Electrical Engineering and Informatics 8(2), 402-412 (2016). https://doi.org/10.15676/jieei.2016.8.2.12 DOI: https://doi.org/10.15676/ijeei.2016.8.2.12

[12] Ilgun, F.Y.: Fuzzy Hypothesis Test for Cognitive Radios. Erzincan University Journal of Science and Technology 14(1), 182-188 (2021). https://doi.org/10.18185/erzifbed.734998 DOI: https://doi.org/10.18185/erzifbed.734998

[13] Abdelbaset, S.E., Kasem, H.M., Khalaf, A.A., Hussein, A.H., Kabeel, A.A.: Deep learning-based spectrum sensing for cognitive radio applications. Sensors 24(24), 7907 (2024). https://doi.org/10.3390/s24247907 DOI: https://doi.org/10.3390/s24247907

[14] Kocakaya, K., Develi, I.: Spectrum sensing in cognitive radio networks: threshold optimization and analysis. EURASIP Journal on Wireless Communications and Networking 2020(1), 255 (2020). https://doi.org/10.1186/s13638-020-01870-7 DOI: https://doi.org/10.1186/s13638-020-01870-7

[15] Gowda, C.M., Vijayakumar, T.: A New Channel Assignment Method in Cognitive Radio System. ICTACT Journal on Communication Technology 9(04), 1885-1892 (2018). https://doi.org/10.21917/ijct.2018.0275 DOI: https://doi.org/10.21917/ijct.2018.0275

[16] Halaki, A., Sarkar, S., Gurugopinath, S., R., M.: Normbased spectrum sensing for cognitive radios under generalised Gaussian noise. IET Networks, 12(6), 282-294 (2023). https://doi.org/10.1049/ntw2.12092 DOI: https://doi.org/10.1049/ntw2.12092

[17] 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 2024(1), 10 (2024). https://doi.org/10.1186/s13638-024-02338-8 DOI: https://doi.org/10.1186/s13638-024-02338-8

[18] Rassomakhin, S., Brifman, J.: Comparison of Two Approaches to Modeling Additive White Gaussian Noise as It Acts on Arbitrary Signals. International Journal of Communications, Network and System Sciences 18(4), 39-50 (2025). https://doi.org/10.4236/ijcns.2025.184004 DOI: https://doi.org/10.4236/ijcns.2025.184004

[19] Valadao, M., Amoedo, D., Costa, A., Carvalho, C., Sabino, W.: Predicting noise and user distances from spectrum sensing signals using transformer and regression models. Applied Sciences 15(8), 4296 (2025). https://doi.org/10.3390/app15084296 DOI: https://doi.org/10.3390/app15084296

[20] Lan, D.T., Ngo, Q.T., Nguyen, L.V., Lee, O.J.: A multi-branch network for cooperative spectrum sensing via attention-based and CNN feature fusion. Scientific Reports 16, 5111 (2026). https://doi.org/10.1038/s41598-026-36031-1 DOI: https://doi.org/10.1038/s41598-026-36031-1

[21] Augustine, Z., Sani, S.M., Tekanyi, A.M.S.: A Review on Cognitive Radio Spectrum Sensing Candidate for 5G and Next Generations, SLU Journal of Science and Technology 2(3), 19-24 (2021)

[22] Raymond, J., Olajumoke, A.O., Francis, M., Usman, A.D.: A review of spectrum sensing times in cognitive radio networks. NIPES - Advances in Engineering Design Technology 5(1), 29-42 (2023). https://doi.org/10.5281/zenodo.7781932

[23] Sherman, M., Mody, A.N., Martinez, R., Rodriguez, C., Reddy, R.: IEEE standards supporting cognitive radio and networks, dynamic spectrum access, and coexistence. IEEE Communications Magazine 46(7), 72-79 (2008). https://doi.org/10.1109/MCOM.2008.4557045 DOI: https://doi.org/10.1109/MCOM.2008.4557045

[24] Hiremath, S., Mishra, A.K., Patra, S.K.: "Engineering review of the IEEE 802.22 standard on cognitive radio" in White Space Communication. Springer, Cham, Switzerland (2014). https://doi.org/10.1007/978-3-319-08747-4.1 DOI: https://doi.org/10.1007/978-3-319-08747-4_1

[25] Idris, M.Y.I., Ahmedy, I., Soon, T.K., Yahuza, M., Tambuwal, A.B., Ali, U.: Cognitive radio and machine learning modalities for enhancing the smart transportation system: A systematic literature review. ICT Express 10(4), 693-734 (2024). https://doi.org/10.1016/j.icte.2024.05.001 DOI: https://doi.org/10.1016/j.icte.2024.05.001

[26] 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 2022(2), 1-12 (2022). https://doi.org/10.1155/2022/2656797 DOI: https://doi.org/10.1155/2022/2656797

[27] Muzaffar, M.U., Sharqi, R.: A review of spectrum sensing in modern cognitive radio networks. Telecommunication Systems 85(2), 347-363 (2024). https://doi.org/10.1007/s11235-023-01079-1 DOI: https://doi.org/10.1007/s11235-023-01079-1

[28] Nasser, A., Mansour, A., Yao, K.C., Abdallah, H.: "Spectrum sensing for half and full-duplex cognitive radio" in Spectrum Access and Management for Cognitive Radio Networks, Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2254-8_2 DOI: https://doi.org/10.1007/978-981-10-2254-8_2

[29] Balakumar, D., Sendrayan, N.: Enhance the probability of detection of cooperative spectrum sensing in cognitive radio networks using blockchain technology. Journal of Electrical and Computer Engineering 2023(1), 8920243 (2023). https://doi.org/10.1155/2023/8920243 DOI: https://doi.org/10.1155/2023/8920243

[30] Pandi, N., Kumar, A.: A review on cognitive radio for next generation cellular network and its challenges. American Journal of Engineering and Applied Sciences 10(2), 334-347 (2017). https://doi.org/10.3844/ajeassp.2017.334.347 DOI: https://doi.org/10.3844/ajeassp.2017.334.347