Skip to main content
SpringerLink
Log in

Deep learning application for sensing available spectrum for cognitive radio: An ECRNN approach

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Spectrum sensing (SS) is a concept of cognitive radio systems at base transceiver stations that can find the white space i.e. licensed spectrum owned by primary users (PU), for transmission over a wireless network without any channel interference. The cognitive radio network is designed to overcome the problem of the limited radio frequency spectrum as most of the applications are dependent on wireless devices in 5G. The major concern that arises here is the detection of spectrum availability. The traditional approaches can solve this issue but consume a large amount of time and prior information about PU and spectrum. The objective of this paper is to give a solution to resolve such issues. In this paper, we have used the learning capabilities of deep learning algorithms such as Convolution neural network (CNN) and Recurrent neural network (RNN) for spectrum sensing without prior knowledge of PU. The proposed model is termed ensemble CNN and RNN (ECRNN) to learn the features of spectrum data and predict the spectrum availability at base transceiver stations in 5G. The simulation result of the ECRNN showed the improvement of accuracy of the system with a reduction in losses that occurred during the false alarm of prediction as well as an improvement in the probability of detection. ECRNN had analyzed PU statistics and result in better spectrum sensing. This paper also supported multiple SUs that would increase the speed of spectrum sensing and data transmission over the available limited spectrum at the same time.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Lundén J, Koivunen V, Poor HV (2015) Spectrum exploration and exploitation for cognitive radio: recent advances. IEEE Signal Process Mag 32:123–140

    Article  Google Scholar 

  2. Wellens M, Mähönen P (2009) Lessons learned from an extensive spectrum occupancy measurement campaign and a stochastic duty cycle model. In: 2009 5th international conference on Testbeds and research infrastructures for the development of networks and communities and workshops, TridentCom 2009. https://doi.org/10.1109/TRIDENTCOM.2009.4976263

  3. Mitola J, Maguire GQ (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun 6:13–18. https://doi.org/10.1109/98.788210

    Article  Google Scholar 

  4. Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23:201–220. https://doi.org/10.1109/JSAC.2004.839380

    Article  Google Scholar 

  5. López-Benítez M, Casadevall F (2011) Modeling and simulation of time-correlation properties of spectrum use in cognitive radio. In: proceedings of the 2011 6th international ICST conference on cognitive radio oriented wireless networks and communications, CROWNCOM 2011. Pp 326–330. https://doi.org/10.4108/icst.crowncom.2011.246158

  6. Haykin S, Thomson DJ, Reed JH (2009) Spectrum sensing for cognitive radio. Proc IEEE 97:849–877. https://doi.org/10.1109/JPROC.2009.2015711

    Article  Google Scholar 

  7. Urkowitz H (1967) Energy detection of unknown deterministic signals. Proc IEEE 55:523–531. https://doi.org/10.1109/PROC.1967.5573

    Article  Google Scholar 

  8. Yücek T, Arslan H (2009) A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutorials 11:116–130. https://doi.org/10.1109/SURV.2009.090109

    Article  Google Scholar 

  9. Wang P, Fang J, Han N, Li H (2010) Multiantenna-assisted spectrum sensing for cognitive radio. IEEE Trans Veh Technol 59:1791–1800. https://doi.org/10.1109/TVT.2009.2037912

    Article  Google Scholar 

  10. Chen X, Zhang H, MacKenzie AB, Matinmikko M (2014) Predicting spectrum occupancies using a non-stationary hidden markov model. IEEE Wirel Commun Lett 3:333–336. https://doi.org/10.1109/LWC.2014.2315040

    Article  Google Scholar 

  11. Letaief KB, Chen W, Shi Y, Zhang J, Zhang YJA (2019) The roadmap to 6G: AI empowered wireless networks. IEEE Commun Mag 57:84–90. https://doi.org/10.1109/MCOM.2019.1900271

    Article  Google Scholar 

  12. Zeng Y, Choo LK, Liang YC (2008) Maximum eigenvalue detection: theory and application. In: IEEE International Conference on Communications. pp. 4160–4164. https://doi.org/10.1109/ICC.2008.781

  13. Zhang R, Lim TJ, Liang YC, Zeng Y (2010) Multi-antenna based spectrum sensing for cognitive radios: a GLRT approach. IEEE Trans Commun 58:84–88. https://doi.org/10.1109/TCOMM.2010.01.080158

    Article  Google Scholar 

  14. Saleem Y, Rehmani MH (2014) Primary radio user activity models for cognitive radio networks: a survey. J Netw Comput Appl 43:1–16

    Article  Google Scholar 

  15. Nguyen T, Mark BL, Ephraim Y (2013) Spectrum sensing using a hidden bivariate markov model. IEEE Trans Wirel Commun 12:4582–4591. https://doi.org/10.1109/TWC.2013.072513.121864

    Article  Google Scholar 

  16. Sarikhani R, Keynia F (2020) Cooperative Spectrum sensing meets machine learning: deep reinforcement learning approach. IEEE Commun Lett 24:1459–1462. https://doi.org/10.1109/LCOMM.2020.2984430

    Article  Google Scholar 

  17. Lees WM, Wunderlich A, Jeavons PJ, Hale PD, Souryal MR (2019) Deep learning classification of 3.5-GHz band spectrograms with applications to spectrum sensing. IEEE Trans Cogn Commun Netw 5:224–236. https://doi.org/10.1109/TCCN.2019.2899871

    Article  Google Scholar 

  18. Liu C, Wang J, Liu X, Liang YC (2019) Deep CM-CNN for Spectrum sensing in cognitive radio. IEEE J Sel Areas Commun 37:2306–2321. https://doi.org/10.1109/JSAC.2019.2933892

    Article  Google Scholar 

  19. Xie J, Liu C, Liang YC, Fang J (2019) Activity pattern aware Spectrum sensing: a CNN-based deep learning approach. IEEE Commun Lett 23:1025–1028. https://doi.org/10.1109/LCOMM.2019.2910176

    Article  Google Scholar 

  20. Xie J, Fang J, Liu C, Li X (2020) Deep learning-based Spectrum sensing in cognitive radio: a CNN-LSTM approach. IEEE Commun Lett 24:2196–2200. https://doi.org/10.1109/LCOMM.2020.3002073

    Article  Google Scholar 

  21. Soni B, Patel DK, Lopez-Benitez M (2020) Long short-term memory based Spectrum sensing scheme for cognitive radio using primary activity statistics. IEEE Access 8:97437–97451. https://doi.org/10.1109/ACCESS.2020.2995633

    Article  Google Scholar 

  22. Paisana F, Selim A, Kist M, Alvarez P, Tallon J, Bluemm C, Puschmann A, Dasilva L (2017) Context-aware cognitive radio using deep learning. In: 2017 IEEE international symposium on dynamic Spectrum access networks, DySPAN 2017. https://doi.org/10.1109/DySPAN.2017.7920784

  23. Cheng Q, Shi Z, Nguyen DN, Dutkiewicz E (2019) Deep learning network based Spectrum sensing methods for OFDM systems. ArXiv. http://arxiv.org/abs/1807.09414

  24. Lee W, Kim M, Cho DH (2019) Deep cooperative sensing: cooperative Spectrum sensing based on convolutional neural networks. IEEE Trans Veh Technol 68:3005–3009. https://doi.org/10.1109/TVT.2019.2891291

    Article  Google Scholar 

  25. Zhang Y, Cai P, Pan C, Zhang S (2019) Multi-agent deep reinforcement learning-based cooperative Spectrum sensing with upper confidence bound exploration. IEEE Access 7:118898–118906. https://doi.org/10.1109/ACCESS.2019.2937108

    Article  Google Scholar 

  26. Kim S, Lee J, Wang H, Hong D (2009) Sensing performance of energy detector with correlated multiple antennas. IEEE Signal Process Lett 16:671–674. https://doi.org/10.1109/LSP.2009.2021381

    Article  Google Scholar 

  27. Zeng Y, Liang YC (2009) Spectrum-sensing algorithms for cognitive radio based on statistical covariances. IEEE Trans Veh Technol 58:1804–1815. https://doi.org/10.1109/TVT.2008.2005267

    Article  Google Scholar 

  28. Oppenheim AV, Editor ANDREWS S, Brigham H, Adaptive Filters CROCHIERE G, Dudgeon R, HAMMING Digital Filters M, Haykin E, Haykin E, Array Signal Processing JAYANT E, Johnson ND, Dudgeon Kay Kay NA, Marple Mcclellan E, Mendel Oppenheim R, Oppenheim E, Oppenheim E, Young Oppenheim W, Rabiner G, Stearns T, Stearns D, Tribolet Vaidyanathan Widrow H,  Kay SM (n.d.) PRENTICE H A L L SIGNAL PROCESSING SERIES Digital Signal Processing OPPENHEIM AND SCHAFER Discrete-Time Signal Processing Fundamentals of Statistical Signal Processing: Est imat ion Theory. Retrieved May 5, 2021, from http://wmn.prenhrll.com

  29. He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp. 5353–5360. https://doi.org/10.1109/CVPR.2015.7299173

  30. Liu C, Liu X, Liang YC (2019) Deep CNN for Spectrum sensing in cognitive radio. In: IEEE International Conference on Communications. Institute of Electrical and Electronics Engineers Inc

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Technology, City University, Malaysia, Petaling Jaya, Malaysia

    S. B. Goyal

  2. Department of Computer Science & Engineering, Lingayas Vidyapeeth, Faridabad, India

    Pradeep Bedi

  3. St Andrews Institute of Technology and Management, Gurgaon, India

    Jugnesh Kumar

  4. Department of CSE, University of New South Wales, Kensington, Australia

    Vijaykumar Varadarajan

Authors
  1. S. B. Goyal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradeep Bedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jugnesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vijaykumar Varadarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. B. Goyal.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Special Issue on Cognitive Models for Peer-to-Peer Networking in 5G and Beyond Networks and Systems

Guest Editors: Anil Kumar Budati, George Ghinea, Dileep Kumar Yadav and R. Hafeez Basha

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goyal, S.B., Bedi, P., Kumar, J. et al. Deep learning application for sensing available spectrum for cognitive radio: An ECRNN approach. Peer-to-Peer Netw. Appl. 14, 3235–3249 (2021). https://doi.org/10.1007/s12083-021-01169-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-021-01169-4

Keywords

Search

Navigation