Research Article | | Peer-Reviewed

Detection of Practical Primary Users in Severe Noise Environments for Cognitive Radio

Received: 21 August 2024     Accepted: 18 September 2024     Published: 31 October 2024
Views:       Downloads:
Abstract

Cognitive radio (CR) is one of the compelling ideas to solve the spectrum scarcity problem for rapid developments in wireless communication systems. In CR systems, signal detection for orthogonal frequency division multiplexing (OFDM) systems in severe noise environments is a key challenge. The area of practical primary user detection has not been explored in depth. The proposed method is an effective method for sensing OFDM applications, which are the practical primary users, for low signal-to-noise (SNR) cases. In the proposed method, the parallel combination of the comb filter and the time-domain autocorrelation function is exploited. The detection performance is measured for various OFDM system applications, including the IEEE 802.11a wireless LAN (WLAN) radio interface, long-term evaluation (LTE), and digital audio broadcasting (DAB) for various CP ratios under 16-quadrature amplitude modulation (16-QAM) and 64-quadrature amplitude modulation (64-QAM) over multipath Rayleigh fading channels with additive white Gaussian noise (AWGN). Furthermore, the OFDM sensing is possible in the presence of noise uncertainty and the sensing performance is compared under consideration with and without noise uncertainty cases. The simulation results demonstrated that our proposed method undoubtedly improves the sensing performances (up to 11 dB SNR gain) of practical primary users more than the conventional spectrum detection methods for low SNR cases.

Published in American Journal of Networks and Communications (Volume 13, Issue 2)
DOI 10.11648/j.ajnc.20241302.12
Page(s) 97-107
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

WLAN, LTE, DAB, Noise Uncertainty, SNR, Cognitive Radio

References
[1] Mitola, J. Cognitive radio: an integrated agent architecture for software defined radio. Ph.D. Dissertation, Royal Institute of Technology, Sweden, 2000.
[2] Haykin, S. Cognitive radio: brain-empowered wireless communications. IEEE Journal of Selected Areas in Communications. 2005, 23(2), 201-220.
[3] Yu, Q. A Survey of Cooperative Games for Cognitive Radio Networks. Wireless Personal Communications. 2013, 73, 949-966.
[4] Huang, H., Mu, J., Jing, X. Cooperative spectrum sensing based on centralized double threshold in MCN. China Communications. 2020, 17(5), 235-242.
[5] Cichon, K., Kliks, A., Bogucka, H. Energy-efficient cooperative spectrum sensing: a survey. IEEE Communications Surveys and Tutorials. 2016, 18(3), 1861-1886.
[6] Lee, W., Kim, M., Cho, D. H. Deep cooperative sensing: cooperative spectrum sensing based on convolutional neural networks. IEEE Transactions on Vehicular Technology. 2019, 68(3), 3005-3009.
[7] Verma, G., Sahu, O. P. A Distance Based Reliable Cooperative Spectrum Sensing Algorithm in Cognitive Radio. Wireless Personal Communications. 2018, 99, 203-212.
[8] Huang, X. L., Xu, Y., Wu, J., Zhang, W. Non- cooperative spectrum sensing with historical sensing data mining in cognitive radio. IEEE Transactions on Vehicular Technology. 2017, 66(10), 8863-8871.
[9] Pati, B. M., Kaneko, M., Taparugssanagorn, A. A deep convolutional neural network based transfer learning method for non-cooperative spectrum sensing. IEEE Access. 2020, 8, 164529-164545.
[10] Bouallegue, K., Crussiere, M., Kharbech, S. SVM assisted primary user-detection for non- cooperative cognitive radio networks. In IEEE Symposium on Computers and Communications (ISCC). Rennes, France, 2020.
[11] Singh, A. K., Ranjan, R. Multiˆ alayer perceptron based spectrum prediction in cognitive radio network. Wireless Personal Communications. 2022, 123, 3539-3553.
[12] Tumuluru, V. K., Wang, P., Niyato, D. Channel status prediction for cognitive radio networks. Wireless Communications and Mobile Computing. 2012, 12, 862- 874.
[13] Bujunuru, A., Srinivasulu, T. A survey on spectrum sensing techniques and energy harvesting. In IEEE International Conference on Recent Innovations in Electrical, Electronics, and Communication Engineering (ICRIEECE). Bhubaneswar, India, 2020.
[14] Gavrilovska, L., Atanasovski, V. Spectrum Sensing Framework for Cognitive Radio Networks. Wireless Personal Communications. 2011, 59, 447-469.
[15] Bhowmick, A., Prasad, B., Roy, S. D., Kundu, S. Performance of cognitive radio network with novel hybrid spectrum access schemes. Wireless Personal Communications. 2016, 91, 541-560.
[16] Awin, F., Raheem, E. A., Tepe, K. Blind spectrum sensing approaches for interweaved cognitive radio system: a tutorial and short course, IEEE Communications Surveys and Tutorials. 2019, 21(1), 238-259.
[17] Prathan, P. M., Panda, G. Information Combining Schemes for Cooperative Spectrum Sensing: A Survey and Comparative Performance Analysis. Wireless Personal Communications. 2017, 94, 685-711.
[18] Ali, A., Hamouda, W. Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Communications Surveys and Tutorials. 2016, 19(2), 1277-1304.
[19] Claudino, L., Abr˜A£o, T. Spectrum Sensing Methods for Cognitive Radio Networks: A Review. Wireless Personal Communications. 2017, 95, 5003-5037.
[20] Chaudhari, S., Kosunen, M., Mäkinen, S., Oksanen, J., Laatta, M., Ojaniemi, J., Koivunen, V., Ryynänen, J. Valkama, M. Performance evaluation of Cyclostationary-Based Cooperative sensing Using field measurements. IEEE Transactions on Vehicular Technology. 2016, 65(4), 1982-1997.
[21] Kay, S. M. Fundamentals of Statistical Signal Processing: Detection Theory. Prentice Hall; 1993.
[22] Zhang, X., Gao, F., Chai, R., Jiang, T. Matched filter based spectrum sensing when primary user has multiple power levels. Cnina Communications. 2015, 12(2), 21- 31.
[23] Digham, F. F., Alouini, M. S., Simon, M. K. On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications. 2007, 55(1), 21-24.
[24] Chatziantoniou, E., Allen, B., Velisavljevic, V., Karadimas, P., Coon, J. Energy detection based spectrum sensing over two-wave with diffuse power fading channels. IEEE Transactions on Vehicular Technology. 2017, 66(1), 868-874.
[25] Sofotasios, P. C., Rebeiz, E., Zhang, L., Tsiftsis, T. A., Cabric, D., Freear, S. Energy detection based spectrum sensing over kappa-mu and kappa- mu extreme fading channels. IEEE Transactions on Vehicular Technology. 2013, 62(3), 1031-1040.
[26] De, P., Liang, Y. C. Blind spectrum sensing algorithms for cognitive radio networks. IEEE Transactions on Vehicular Technology. 2008, 57(5), 2834-2842.
[27] Han, W., Huang, C., Li, J., Li, Z., Cui, S. Correlation-based spectrum sensing with oversampling in cognitive radio. IEEE Journal of Selected Areas in Communications. 2015, 33(5), 788-802.
[28] Lunden, J., Kassam, S. A., Koivunen, V. Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio. IEEE Transactions on Signal Processing. 2010, 58(1), 38-52.
[29] Chaudhari, S., Koivunen, V., Poor, H. V. Distributed autocorrelation-based sequential detection of OFDM signals in cognitive radios. In Proceedings of IEEE International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM). Singapore, 2008; pp. 1-6.
[30] Chaudhari, S., Koivunen, V., Poor, H. V. Autocorrelation- based decentralized sequential detection of OFDM signals in cognitive radios. IEEE Transactions on Signal Processing. 2009, 57(7), 2690-2700.
[31] Chambers, P., Sellathurai, M. Implementation of an autocorrelation-based spectrum sensing algorithm in real-world channels with frequency offset. In Proceedings of IEEE Sensor Signal Processing for Defense (SSPD). Edinburgh, UK, 2014; pp. 1-5.
[32] Chin, W. L., Kao, C. W., Qian, Y. Spectrum sensing of OFDM systems over multipath fading channelsandpracticalconsiderationsforcognitiveradios. IEEE Sensors Journal. 2016, 16(8), 2349-2360.
[33] Haque, M, Shimamura, T. Performance evaluation of spectrum sensing for OFDM systems using parallel combination of comb filter and autocorrelator. International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE: Ishigaki, Japan, 2019; pp. 111-116.
[34] Socheleau, F. X., Bey, A. A. E., Houcke, S. Non data-aided SNR estimation of OFDM signals. IEEE Communications Letters. 2008, 12(11), 813-815.
[35] Talbot, S. L., Boroujeny, B. F. Spectral method of blind carrier tracking for OFDM. IEEE Transactions on Signal Processing. 2008, 56(7), 2706-2717.
[36] Hong, E., Kim, K., Har, D. Spectrum sensing by parallel pairs of cross-correlations and comb filters for OFDM systems with pilot tones. IEEE Sensors Journal. 2012, 12(7), 2380-2383.
[37] Tandra, R., Sahai, A. SNR walls for signal detection. IEEE Journal of Selected Topics in Signal Processing. 2008, 2, 4-17.
Cite This Article
  • APA Style

    Haque, M., Sugiura, Y., Shimamura, T. (2024). Detection of Practical Primary Users in Severe Noise Environments for Cognitive Radio. American Journal of Networks and Communications, 13(2), 97-107. https://doi.org/10.11648/j.ajnc.20241302.12

    Copy | Download

    ACS Style

    Haque, M.; Sugiura, Y.; Shimamura, T. Detection of Practical Primary Users in Severe Noise Environments for Cognitive Radio. Am. J. Netw. Commun. 2024, 13(2), 97-107. doi: 10.11648/j.ajnc.20241302.12

    Copy | Download

    AMA Style

    Haque M, Sugiura Y, Shimamura T. Detection of Practical Primary Users in Severe Noise Environments for Cognitive Radio. Am J Netw Commun. 2024;13(2):97-107. doi: 10.11648/j.ajnc.20241302.12

    Copy | Download

  • @article{10.11648/j.ajnc.20241302.12,
      author = {Mousumi Haque and Yosuke Sugiura and Tetsuya Shimamura},
      title = {Detection of Practical Primary Users in Severe Noise Environments for Cognitive Radio},
      journal = {American Journal of Networks and Communications},
      volume = {13},
      number = {2},
      pages = {97-107},
      doi = {10.11648/j.ajnc.20241302.12},
      url = {https://doi.org/10.11648/j.ajnc.20241302.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20241302.12},
      abstract = {Cognitive radio (CR) is one of the compelling ideas to solve the spectrum scarcity problem for rapid developments in wireless communication systems. In CR systems, signal detection for orthogonal frequency division multiplexing (OFDM) systems in severe noise environments is a key challenge. The area of practical primary user detection has not been explored in depth. The proposed method is an effective method for sensing OFDM applications, which are the practical primary users, for low signal-to-noise (SNR) cases. In the proposed method, the parallel combination of the comb filter and the time-domain autocorrelation function is exploited. The detection performance is measured for various OFDM system applications, including the IEEE 802.11a wireless LAN (WLAN) radio interface, long-term evaluation (LTE), and digital audio broadcasting (DAB) for various CP ratios under 16-quadrature amplitude modulation (16-QAM) and 64-quadrature amplitude modulation (64-QAM) over multipath Rayleigh fading channels with additive white Gaussian noise (AWGN). Furthermore, the OFDM sensing is possible in the presence of noise uncertainty and the sensing performance is compared under consideration with and without noise uncertainty cases. The simulation results demonstrated that our proposed method undoubtedly improves the sensing performances (up to 11 dB SNR gain) of practical primary users more than the conventional spectrum detection methods for low SNR cases.},
     year = {2024}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Detection of Practical Primary Users in Severe Noise Environments for Cognitive Radio
    AU  - Mousumi Haque
    AU  - Yosuke Sugiura
    AU  - Tetsuya Shimamura
    Y1  - 2024/10/31
    PY  - 2024
    N1  - https://doi.org/10.11648/j.ajnc.20241302.12
    DO  - 10.11648/j.ajnc.20241302.12
    T2  - American Journal of Networks and Communications
    JF  - American Journal of Networks and Communications
    JO  - American Journal of Networks and Communications
    SP  - 97
    EP  - 107
    PB  - Science Publishing Group
    SN  - 2326-8964
    UR  - https://doi.org/10.11648/j.ajnc.20241302.12
    AB  - Cognitive radio (CR) is one of the compelling ideas to solve the spectrum scarcity problem for rapid developments in wireless communication systems. In CR systems, signal detection for orthogonal frequency division multiplexing (OFDM) systems in severe noise environments is a key challenge. The area of practical primary user detection has not been explored in depth. The proposed method is an effective method for sensing OFDM applications, which are the practical primary users, for low signal-to-noise (SNR) cases. In the proposed method, the parallel combination of the comb filter and the time-domain autocorrelation function is exploited. The detection performance is measured for various OFDM system applications, including the IEEE 802.11a wireless LAN (WLAN) radio interface, long-term evaluation (LTE), and digital audio broadcasting (DAB) for various CP ratios under 16-quadrature amplitude modulation (16-QAM) and 64-quadrature amplitude modulation (64-QAM) over multipath Rayleigh fading channels with additive white Gaussian noise (AWGN). Furthermore, the OFDM sensing is possible in the presence of noise uncertainty and the sensing performance is compared under consideration with and without noise uncertainty cases. The simulation results demonstrated that our proposed method undoubtedly improves the sensing performances (up to 11 dB SNR gain) of practical primary users more than the conventional spectrum detection methods for low SNR cases.
    VL  - 13
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Sections