SpringerLink
Log in

Intelligent spectrum sensing algorithm for cognitive internet of vehicles based on KPCA and improved CNN

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

Abstract

With the acceleration of economic globalization and integration, the global trade is becoming more frequent, which promotes the vigorous development of transportation industry. In recent years, the Internet of Vehicle (IoV) has developed rapidly in the transportation industry, and the number of IoV users has exploded. The requirements for IoV communication services are very high, resulting in the lack of spectrum resources. Rather than utilizing traditional spectrum resource allocation methods, cognitive radio technology makes full use of idle frequency bands, improving the IoV communication spectrum’s utilization rate. Spectrum sensing is the primary link to realize a cognitive radio. However, IoV mobile communication environment is characterized by complexity, dynamism, and substantial noise interference, thus imposing significant challenges to spectrum sensing. Thus, this paper proposes an intelligent spectrum sensing algorithm based on kernel principal component analysis (KPCA) and an improved convolutional neural network (CNN). Since the wireless signal cannot distinguish the signal and noise linearly, KPCA maps the sampled signal to a high-dimensional space, creates a covariance matrix, and obtains eigenvector data of the signal and noise through matrix decomposition. A spectrum sensing classifier based on improved CNN is proposed, and the dynamic threshold is obtained via offline training. Compared with the traditional algorithm, the designed deep CNN improves the model’s training speed, enables parameter sharing, and reduces the number of model parameters, effectively reducing the computational complexity. Additionally, due to the extracted signal feature’s small dimension, the algorithm reduces the number of pooling layers and avoids the effective features’ loss, thus increasing the detection probability. Finally, the proposed algorithm achieves a 10% higher sensing accuracy than support vector machine (SVM), Elman, and LeNet5 algorithms, signaling its robustness.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

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

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Singh PK, Singh R, Nandi SK, Ghafoor KZ, Rawat DB, Nandi S (2021) Blockchain-based adaptive trust management in Internet of Vehicles using smart contract. IEEE Trans Intell Transp Syst 22(6):3616–3630

    Article  Google Scholar 

  2. Kim Y, Lee TJ (2019) V2IoT communication systems for road safety. IEEE Wirel Commun Lett 8(5):1460–1463

    Article  MathSciNet  Google Scholar 

  3. Hisham A, Yuan D, Ström EG, Brännström F (2021) Adjacent channel interference aware joint scheduling and power control for V2V broadcast communication. IEEE Trans Intell Transp Syst 22(1):443–456

    Article  Google Scholar 

  4. Liu R, Yu G, Yuan J, Li GY (2021) Resource management for millimeter-wave ultra-reliable and low-latency communications. IEEE Trans Commun 69(2):1094–1108

    Google Scholar 

  5. Tashman DH, Hamouda W (2021) An overview and future directions on physical-layer security for cognitive radio networks. IEEE Network 35(3):205–211

    Article  Google Scholar 

  6. Hasan C, Marina MK (2019) Communication-free inter-operator interference management in shared spectrum small cell networks. IEEE Trans Cognit Commun Netw 5(3):661–677

    Article  Google Scholar 

  7. Pei E, Zhou L, Deng BG, Lu X, Li Y, Zhang ZZ (2021) A Q-learning based energy threshold optimization algorithm in LAA networks. IEEE Trans Veh Technol 70(7):7037–7049

    Article  Google Scholar 

  8. Liu X, Sun Q, Lu W, Wu C, Ding H (2020) Big-data-based intelligent spectrum sensing for heterogeneous spectrum communications in 5G. IEEE Wirel Commun 27(5):67–73

    Article  Google Scholar 

  9. Lee S, Park SR, Kim YH, Song I (2021) Spectrum sensing for cognitive radio network with multiple receive antennas under impulsive noise environments. J Commun Netw 23(3):171–179

    Article  Google Scholar 

  10. Gharib A, Ejaz W, Ibnkahla M (2021) Distributed spectrum sensing for IoT networks: architecture, challenges, and learning. IEEE Internet Things 4(2):66–73

    Article  Google Scholar 

  11. Zhang S, Wang Y, Zhang Y, Wan P, Zhuang J (2022) Riemannian distance-based fast K-Medoids clustering algorithm for cooperative spectrum sensing. IEEE Syst J 16(1):880–890

  12. Shi Z, Gao W, Zhang S, Liu J, Kato N (2020) Machine learning-enabled cooperative spectrum sensing for non-orthogonal multiple access. IEEE Trans Wirel Commun 19(9):5692–5702

    Article  Google Scholar 

  13. Xu Y, Cheng P, Chen Z, Li Y, Vucetic B (2018) Mobile collaborative spectrum sensing for heterogeneous networks: a Bayesian machine learning approach. IEEE Trans Signal Process 66(21):5634–5647

    Article  MathSciNet  MATH  Google Scholar 

  14. Zerhouni K, Amhoud EM, Chafii M (2021) Filtered multicarrier waveforms classification: a deep learning-based Approach. IEEE Access 9:69426–69438

    Article  Google Scholar 

  15. Cao WG, Zhang JN, Cai CX, Chen Q, Zhao Y, Lou Y, Jiang W, Gui G (2021) CNN-based intelligent safety surveillance in green IoT applications. China Commun 18(1):108–119

    Article  Google Scholar 

  16. Xu LW, Zhou XP, Tao Y, Liu L, Yu X, Kumar N (2022) Intelligent security performance prediction for IoT-enabled healthcare networks using improved CNN. IEEE Trans Ind Inform 18(3):2063–2074

  17. Fang J, Wang B, Li H, Liang YC (2021) Recent advances on sub-nyquist sampling-based wideband spectrum sensing. IEEE Wirel Commun 28(3):115–121

    Article  Google Scholar 

  18. Qin ZJ, Li GY (2020) Pathway to intelligent radio. IEEE Wirel Commun 27(1):9–15

    Article  Google Scholar 

  19. Liu X, Lin B, Zhou M, Jia M (2021) NOMA-based cognitive spectrum access for 5G-enabled Internet of Things. IEEE Netw 35(5):290–297

  20.  Shruti S, Rakhee R (2021) Analysis of spectrum sensing and spectrum access in cognitive radio networks with heterogeneous traffic and p-Retry Buffering. IEEE Trans Mob Comput 21(7):2318–2331. https://doi.org/10.1109/TMC.2020.3042836

  21. **ao Z, Shen XY, Zeng FZ, Havyarimana V, Wang D, Chen WW, Li KQ (2018) Spectrum resource sharing in heterogeneous vehicular networks: a noncooperative game-theoretic approach with correlated equilibrium. IEEE Trans Veh Technol 67(10):9449–9458

    Article  Google Scholar 

  22. Hamedani K, Liu L, Yi Y (2021) Energy efficient MIMO-OFDM spectrum sensing using deep stacked spiking delayed feedback reservoir computing. IEEE Trans Green Commun Netw 5(1):484–496

    Article  Google Scholar 

  23. Joshi H, Darak SJ, Kumar AA, Kumar R (2019) Throughput optimized non-contiguous wideband spectrum sensing via online learning and sub-Nyquist sampling. IEEE Wirel Commun Lett 8(3):805–808

    Article  Google Scholar 

  24. Gu Y, Pei Q, Li H (2019) Dynamic matching-based spectrum detection in cognitive radio networks. China Commun 16(4):47–58

    Google Scholar 

  25. Javaid N, Sher A, Nasir H, Guizani N (2018) Intelligence in IoT-Based 5G networks: opportunities and challenges. IEEE Commun Mag 56(10):94–100

    Article  Google Scholar 

  26. Shi S et al (2021) Challenges and new directions in securing spectrum access systems. IEEE Internet Things J 8(8):6498–6518

    Article  Google Scholar 

  27. Wang J, Zhang B, Jia L, Zhao B, Guo D (2020) A distributed collaborative game-theoretic approach in cognitive satellite communication networks. IEEE Access 8:129446–129460

    Article  Google Scholar 

  28. Wang D, Qi P, Fu Q, Zhang N, Li Z (2021) Multiple high-order cumulants-based spectrum sensing in full-duplex-enabled cognitive IoT networks. IEEE Internet Things J 8(11):9330–9343

    Article  Google Scholar 

  29. Liu X, Sun C, Zhou M, Lin B, Lim Y (2021) Reinforcement learning based dynamic spectrum access in cognitive Internet of Vehicles. China Commun 18(7):58–68

    Article  Google Scholar 

  30. Xu W, Wang S, Yan S, He J (2019) An efficient wideband spectrum sensing algorithm for unmanned aerial vehicle communication networks. IEEE Internet Things J 6(2):1768–1780

    Article  Google Scholar 

  31. Zhou X, Sun M, Li GY, Juang BHF (2018) Intelligent wireless communications enabled by cognitive radio and machine learning. China Commun 15(12):16–48

    Google Scholar 

  32. Khalek NA, Hamouda W (2021) From cognitive to intelligent secondary cooperative networks for the future internet: design, advances, and challenges. IEEE Network 35(3):168–175

    Article  Google Scholar 

  33. Yang H, **e X, Kadoch M (2020) Machine learning techniques and a case study for intelligent wireless networks. IEEE Network 34(3):208–215

    Article  Google Scholar 

  34. Mollah MB, Zhao J, Niyato D, Guan YL, Yuen C, Sun S, Lam KY, Koh LH (2021) Blockchain for the Internet of Vehicles towards intelligent transportation systems: a survey. IEEE Internet Things J 8(6):4157–4185

    Article  Google Scholar 

  35. Rathee G, Ahmad F, Kurugollu F, Azad MA, Iqbal R, Imran M (2021) CRT-BIoV: a cognitive radio technique for blockchain-enabled Internet of Vehicles. IEEE Trans Intell Transp Syst 22(7):4005–4015

    Article  Google Scholar 

Download references

Funding

This research was supported by the National Natural Science Foundation of China (No. 62201313), the Open Project of Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing (Yango University)(No.FKLSIPIP1009), and the Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University(No.FKLBDAITI202206).

Author information

Authors and Affiliations

  1. Department of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

    Yanyan Duan & Lingwei Xu

  2. Fujian Key Laboratory of Spatial Information Perception and Intelligent Processing (Yango University), Fuzhou, 350015, China

    Yanyan Duan, Fenghua Huang & Lingwei Xu

  3. Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyishan, China

    Yanyan Duan & Lingwei Xu

  4. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, V8W 2Y2, Canada

    T. Aaron Gulliver

Authors
  1. Yanyan Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fenghua Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lingwei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. Aaron Gulliver
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yanyan Duan: Development of methodology; creation of models; designing computer programs; Validation;

Fenghua Huang: Writing—Review & Editing, Supervision, Funding acquisition, reviewed the manuscript;

Lingwei Xu: Conceptualization, Writing—Review & Editing, Supervision, Funding acquisition, reviewed the manuscript, formal analysis;

T. Aaron Gulliver: Writing—Review & Editing, reviewed the manuscript.

Corresponding authors

Correspondence to Fenghua Huang or Lingwei Xu.

Ethics declarations

Ethical approval

Not applicable.

Consent to publication

All authors have read and agreed to the published version of the manuscript.

Conflict of interest

The authors declared that there is no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Duan, Y., Huang, F., Xu, L. et al. Intelligent spectrum sensing algorithm for cognitive internet of vehicles based on KPCA and improved CNN. Peer-to-Peer Netw. Appl. 16, 2202–2217 (2023). https://doi.org/10.1007/s12083-023-01501-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-023-01501-0

Keywords

Search

Navigation