Throughput Optimization Using Spectrum Sensing Vertical Hypotheses Integrated With F-OFDM Technique

Authors

  • Zakka Augustine Telecommunication Engineering Department, Air Force Institute of Technology, Kaduna 800282, Nigeria Author https://orcid.org/0000-0002-7366-7071
  • Christopher Alabi Telecommunication Engineering Department, Air Force Institute of Technology, Kaduna 800282, Nigeria Author https://orcid.org/0009-0001-8704-385X
  • Franklin Chibueze Njoku Telecommunication Engineering Department, Air Force Institute of Technology, Kaduna 800282, Nigeria Author https://orcid.org/0000-0002-4765-1498
  • Agbotiname Lucky Imoize Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos 100213, Nigeria Author
  • Jerry Raymond Telecommunication Engineering Department, Air Force Institute of Technology, Kaduna 800282, Nigeria Author https://orcid.org/0009-0004-1038-3305

DOI:

https://doi.org/10.64509/jicn.21.50

Keywords:

Centralized Cooperative Spectrum Sensing, Cognitive Links, F-OFDM, Interference Links, Null Detection, Vertical Hypothesis Uncertainty

Abstract

The convergence of Satellite Mobile Networks (SMNs) with Terrestrial Wireless Networks (TWNs) has emerged as a significant area of research. Such integration is crucial for overcoming the current challenges of static spectrum assignment faced by earlier wireless network generations, including inefficient energy utilisation, insufficient bandwidth, increased latency, reduced reliability, limited connectivity, and restricted capacity. To address these issues, the potential of Cognitive Radio Networks (CRNs) has been exploited in Fifth Generation (5G), facilitating seamless interoperability among networks to sustain wireless communications. This paper presents a novel Vertical Hypothesis Uncertainty (VHU) method for optimising system throughput in CRN, leveraging the spectrum sensing false alarm, Pfa  and null detection, Pnd  hypotheses. This approach integrates the Filtered Orthogonal Frequency Division Multiplexing (F-OFDM) with Spectrum Sensing (SS) within a Satellite-Terrestrial Network (STN) domain. The performance of the proposed VHU was tested against the Hybrid Filter Detection with Inverse Covariance (HFDIC) and traditional spectrum sensing concepts. Results at –10 dB Signal-to-Noise Ratio (SNR) show that the VHU method remarkably outperforms HFDIC, achieving a 7.9% improvement in a fixed channel and a 15.84% improvement in a dynamic channel under perfect channel conditions. 

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References

[1] Idris, A., Deros, N.A.M., Taib, I., Kassim, M., Rozaini, M.D., Ali, D.M.: PAPR reduction using Huffman and arithmetic coding techniques in F-OFDM system. Bulletin of Electrical Engineering and Informatics 7(2), 257–263 (2018). http://doi.org/10.11591/eei.v7i2.1169

[2] Gemay, E., Lebda, A.: A cooperative cognitive radio spectrum sensing based on correlation sum method with linear equalization. Communications and Network 15(1), 1–4 (2023). https://doi.org/10.4236/cn.2023.151001

[3] Rajavel, S.E., Devaraj, S.A., Roobert, A.A., Kumar, O.P., Vincent, S.: Energy efficient relay selection framework for 5G communication using cognitive radio networks. Scientific Reports 15(1), 15566 (2025). https://doi.org/10.1038/s41598-025-00068-5

[4] Li, F., Li, Z., Li, G., Dong, F., Zhang, W.: Efficient wideband spectrum sensing with maximal spectral efficiency for LEO mobile satellite systems. Sensors 17(1), 193 (2017). https://doi.org/10.3390/s17010193

[5] Alabi, C.A., Imoize, A.L., Giwa, M.A., Faruk, N., Tersoo, S.T., Ehime, A.E.: Artificial intelligence in spectrum management: policy and regulatory considerations. In 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), pp. 1–6 (2023). https://doi.org/10.1109/ICMEAS58693.2023.10379314

[6] 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

[7] Jia, M., Wang, L., Yin, Z., Guo, Q., Gu, X.: A novel spread slotted ALOHA based on cognitive radio for satellite communications system. EURASIP Journal on Wireless Communications and Networking 2016, 232 (2016). https://doi.org/10.1186/s13638-016-0737-7

[8] 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

[9] Yang, M., Shao, X., Xue, G., Xie, B.: Big data theory based spectrum sensing algorithm for the satellite cognitive radio network. Wireless Networks 30(5), 3911–3919 (2024). https://doi.org/10.1007/s11276-021-02808-7

[10] Agus, S., Nana, R.S., Andriyan, B.S.: A Blind Spectrum Sensing for Cognitive Radio Based on. International Journal on Electrical Engineering and Informatics 8(2), 406–412 (2016). https://doi.org/10.15676/ijeei.2016.8.2.12

[11] Wang, K., Chen, Y., Bo, D., Wang, S.: A novel multiuser collaborative cognitive radio spectrum sensing model: Based on a CNN-LSTM model. PloS one 20(1), e0316291 (2025). https://doi.org/10.1371/journal.pone.0316291

[12] Panda, S.B., Swain, P.K., Imoize, A.L., Tripathy, S.S., Lee, C.C.: A Robust Spectrum Allocation Framework Towards Inference Management in Multichannel Cognitive Radio Networks. International Journal of Communication Systems 38(5), e6057 (2025). https://doi.org/10.1002/dac.6057

[13] 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, pp. 15–50. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2254-8_2

[14] 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(1), 2656797 (2022). https://doi.org/10.1155/2022/2656797

[15] Alabi, C.A., Idakwo, M.A., Imoize, A.L., Adamu, T., Sur, S.N.: AI for spectrum intelligence and adaptive resource management in Artificial Intelligence for Wireless Communication Systems, pp. 57–83. CRC Press, Boca Raton, USA (2024). https://doi.org/10.1201/9781003517689

[16] 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

[17] 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

[18] Fraz, M., Muslam, M.M.A., Hussain, M., Amin, R., Xie, J.: Smart sensing enabled dynamic spectrum management for cognitive radio networks. Frontiers in Computer Science 5, 1271899 (2023). https://doi.org/10.3389/fcomp.2023.1271899

[19] Aswatha, R., Seethalakshmi, V., Murugan, K., Sathishkumar, N., Reethika, A., Gunanandhini, 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

[20] 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 137(1), 813–825 (2023). https://doi.org/10.32604/cmes.2023.027595

[21] Bai, W., Zheng, G., Xia, W., Mu, Y., Xue, Y.: Multi-User Opportunistic Spectrum Access for Cognitive Radio Networks Based on Multi-Head Self-Attention and Multi-Agent Deep Reinforcement Learning. Sensors 25(7), 2025 (2025). https://doi.org/10.3390/s25072025

[22] Kumar, A., Gaur, N., Nanthaamornphong, A.: Hybridized spectrum sensing using neural network-based MF and ED for enhanced detection in Rayleigh channel. Journal of Electrical and Computer Engineering 2025(1), 9506922 (2025). https://doi.org/10.1155/jcec/9506922

[23] Yucek, T., Arslan, H.: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials 11(1), 116–130 (2009). https://doi.org/10.1109/SURV.2009.090109

[24] Patil, R.B., Kulat, K.D., Gandhi, A.S.: SDR based energy detection spectrum sensing in cognitive radio for real time video transmission. Modelling and Simulation in Engineering 2018(1), 2424305 (2018). https://doi.org/10.1155/2018/2424305

[25] Kanti, J., Tomar, G.S.: Various sensing techniques in cognitive radio networks: a review. International Journal of Grid and Distributed Computing 9(1), 145–154 (2016). http://doi.org/10.14257/ijgdc.2016.9.1.15

[26] 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

[27] Kumar, A., Nanthaamornphong, A., Masud, M.: RNN-Bi-LSTM spectrum sensing algorithm for NOMA waveform with diverse channel conditions. Scientific Reports 15(1), 31022 (2025). https://doi.org/10.1038/s41598-025-16414-6

[28] Augustine, Z., Yaro, A.S., Tekanyi, A.M.S., Bello, H., Abdu-Agye, U.F., Agbo, E.E.: Feedback Filtered-OFDM Waveform Candidature for Interference Mitigation in 5G Networks and Beyond. International Journal of Integrated Engineering 17(1), 323–339 (2025). https://doi.org/10.30880/ijie.2025.17.01.027

[29] Universities, E., Makalesi, A., 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

[30] Li, F., Li, Z., Li, G., Dong, F., Zhang, W.: Efficient Wideband Spectrum Sensing with Maximal Spectral Efficiency for LEO mobile satellite systems. Sensors 17(1), 193 (2017). https://doi.org/10.3390/s17010193

[31] Boddukuri, N.K., Pal, D., Bandyopadhyay, A.K., Koley, C.: Adaptive Sampling Point and Q-Learning-Based Sensing Threshold for Spectrum Energy Detection in Cognitive Radio Networks. International Journal of Communication Systems 38(3), e6090 (2025). https://doi.org/10.1002/dac.6090

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Published

2026-01-26

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How to Cite

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

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