iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/978-3-031-40564-8_16
An Innovative AI Architecture for Detecting the Primary User in the Spectrum | SpringerLink
Skip to main content

An Innovative AI Architecture for Detecting the Primary User in the Spectrum

  • Conference paper
  • First Online:
Computing Science, Communication and Security (COMS2 2023)

Abstract

The demand for spectrum is rising by the day as the number of consumers using the spectrum increases. However, the spectrum’s coverage is constrained to a certain area dependent on the local population. Then, researchers come up with an idea of allocating secondary users in the spectrum in the absence of primary users. For this process, a new scheme has been raised known as spectrum sensing in which the primary user’s presence using a variety of procedures. The device used for this process is called Cognitive radio. The spectrum sensing process involves gathering the signal features from the spectrum and then a threshold will be set depending on those values. With this threshold, the final block in Cognitive radio will decide whether the primary user is present or not. The techniques that are involved in spectrum sensing are energy detection, matched filtering, correlation, etc. These techniques cause a reduction in the probability of detection and involve a complex process to sense the spectrum. To overcome these drawbacks, the optimal signal is constructed from the original signal, and this, the spectrum is sensed. This process provides better results in terms of the probability of detection. To increase the scope of the research, the entropy features are extracted and trained with an LSTM based deep learning architecture. This trained network is tested with hybrid a feature which is a combination of both power-optimized features and entropy features. This process derives the spectrum status along with the accuracy and loss curves. The proposed method reduces complexity in sensing the spectrum along with that it produces an accuracy of 99.9% and the probability of detection of 1 at low PSNR values, outcomes when compared to cutting-edge techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Centre for Remote Imaging, Sensing and Processing (CRISP). https://crisp.nus.edu.sg/~research/tutorial/em.htm. Accessed 13 Feb 2023

  2. Electronic notes, ‘Rayleigh Fading’. https://www.electronics-notes.com. Accessed 13 Feb 2023

  3. Wu, J., Luo, T., Yue, G.: An energy detection algorithm based on double-threshold in cognitive radio systems. In: International Conference on Information Science and Enginneering, pp. 493–496. Zhanjiajie (2009)

    Google Scholar 

  4. Maleki, S., Pandharipande, A., Leus, G.: Two-degree spectrum sensing for cognitive radios. In: IEEE International Conference on Acoustics, Spe. and Sig. Proc., IEEE, pp. 2946–2949, Texas, USA (2010)

    Google Scholar 

  5. Plata, D.M.M., Reatiga, A.G.A.: Evaluation of strength detection for spectrum sensing based at the dynamic desire of detection threshold. J. Procedia Eng. 35, 135–143 (2012)

    Article  Google Scholar 

  6. Zhang, X., Chai, R., Gao, F.: Matched cleanout based spectrum sensing and energy degree detection for the cognitive radio network. In: IEEE Global Conference on Signal and Information Processing, IEEE, pp. 1267–1270, Atlanta (2014)

    Google Scholar 

  7. Sutanu, G.: Performance analysis based on a comparative study between multipath Rayleigh fading and AWGN channel in the presence of various interference. Int. J. of Mob. Netw. Commun. Telematics, 4, 15–22 (2014)

    Google Scholar 

  8. Subramaniam, S., Reyes, H., Kaabouch, N.: Spectrum occupancy measurement: an autocorrelation based scanning approach using USRP. In: IEEE Wireless and Microwave Technology Conference on IEEE, pp. 1–5, Barcelona (2015)

    Google Scholar 

  9. Al-Badrawi, M.H., Kirsch, N.J.: An EMD-based double threshold detector for spectrum sensing in cognitive radio networks. In: Vehicular Technology Conference. IEEE, pp. 1–5. Glasgow, UK (2015)

    Google Scholar 

  10. Sai Suneel, A., Prasanthi, K.: Multiple input multiple output cooperative communication technique using for spectrum sensing in cognitive radio network. In: IEEE Int. Conference on Signal Processing Communication Power and Embedded System, IEEE, pp. 2052–2063, Chennai (2016)

    Google Scholar 

  11. Arjoune Y., Mrabet Z. E., Ghazi H. E. and Tamtaoui A. : Spectrum sensing: Enhanced energy detection technique based on noise measurement. In: IEEE 8th Annual Computing and Communication Workshop and Conference, IEEE, pp. 828–834, Las Vegas (2018)

    Google Scholar 

  12. Arjoune, Y., Kaabouch, N.: On spectrum sensing, a machine learning method for cognitive radio systems. In: IEEE International Conference on Electro Information Technology IEEE, pp. 333–338. Ecuador (2019)

    Google Scholar 

  13. Sai Suneel, A., Shiyamala, D.S.: A novel energy detection of spectrum based on noise measurement a review. J. Adv. Res. Dyn. Control Syst. 11, 870–873 (2019)

    Google Scholar 

  14. Sai Suneel, A., Shiyamala, D.S.: Dynamic threshold selection through noise variance for spectrum sensing. Int. J. Eng. Adv. Tech. 8, 23–234 (2019)

    Google Scholar 

  15. Sai Suneel, A., Shiyamala, S.: Peak detection based energy detection of a spectrum under Rayleigh fading noise environment. J. Ambient Intell. Humanized Comput. 12, 4237–4245 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Sai Suneel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Suneel, A.S., Shiyamala, S. (2023). An Innovative AI Architecture for Detecting the Primary User in the Spectrum. In: Chaubey, N., Thampi, S.M., Jhanjhi, N.Z., Parikh, S., Amin, K. (eds) Computing Science, Communication and Security. COMS2 2023. Communications in Computer and Information Science, vol 1861. Springer, Cham. https://doi.org/10.1007/978-3-031-40564-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-40564-8_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40563-1

  • Online ISBN: 978-3-031-40564-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics