Skip to main content

Advertisement

SpringerLink
Log in

Energy conserving relay assistance for reporting users in cognitive radio networks

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

In Cognitive Radio (CR) networks, due to fading and shadowing the consistency of single user sensing is deeply affected thereby degrading its detection performance. Cooperative Spectrum Sensing (CSS) paves the way for improving the reliability of sensing in CR networks. In this work, an energy conserving relay assistance method is proposed for modifying the soft fusion with improved Equal Gain Combining (EGC) method to balance the energy consumption among the reporting users. The residual energies of the reporting users are conserved in such a way that reporting is assisted by nearby non reporting users. The overall network energy consumption is also not affected by the proposed method. Also the improved EGC soft fusion method is compared and analysed with the conventional AND hard fusion method and the detection performance is observed to be improved in the improved soft fusion method. In addition to energy conservation, complexity and missed detection analysis are carried out for the proposed algorithm. MATLAB based simulations are performed to compare and justify the algorithm proposed and to estimate the energy conservation analysis.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

Abbreviations

B j :

Single bit decision of jth SU

E egc :

Global test statistics of EGC fusion

E impegc :

Global test statistics for improved EGC

E j :

Local test statistics of jth SU

E rj :

Reporting energy of jth SU to FC

Errj :

Reporting energy of R-RL path to FC

G :

Constant

H o :

Binary hypothesis denoting PU is absent

H 1 :

Binary hypothesis denoting PU is present

L :

Number of samples in the PU signal received

M :

Number of results forwarded to FC in improved EGC method

N :

Number of cooperating SUs

NFC avg :

Mean distance between non-reporting users and FC

NFC j :

Distance of jth non-reporting user from FC

P dj :

Probability of detection of jth SU

P fj :

Probability of false alarm of jth SU

P lfcj :

Reporting power of jth relay to FC

P r :

Reporting power for any user

P rfcj :

Reporting power of jth SU to FC

P rlj :

Reporting power of jth reporting user to relay

Q(.) :

Standard Q function

Q -1 (.) :

Inverse Q function

Q d :

Global detection probability

Q fa :

Global false alarm probability

Q md :

Global missed detection probability

R :

Long distance reporting users requiring relay assistance

R d :

Reporting distance for any user

RFC avg :

Mean distance between reporting users and FC

RFC j :

Distance of jth reporting user from FC

RL :

Relay users

SNR avg :

Entire CR network’s average SNR

SU j :

jth SU

T egc :

Global threshold for EGC fusion

T F :

Total time frame

T impegc :

Final threshold for improved EGC

T r :

Reporting slot

T s :

Sensing duration

T t :

Data transmission slot

T u :

Reporting duration of a single user

V :

No. of antennas in each SU transceiver

X :

Test statistics to be compared with threshold

ϒ j :

SNR of jth SU

σ r 2 :

Variance of noise

References

  1. Bhandari S and Joshi S 2018 Cognitive radio technology in 5G wireless communications. In: Proceedings of the 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1115–1120

  2. Muchandi N and Khanai R 2016 Cognitive radio spectrum sensing: a survey. In: Proceedings of the International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 3233–3237

  3. Song M, Xin C, Zhao Y and Cheng X 2012 Dynamic spectrum access: from cognitive radio to network radio. IEEE Wireless Commun. 19: 23–29.

    Article  Google Scholar 

  4. Alom M Z, Godder T K and Morshed M N 2015 A survey of spectrum sensing techniques in cognitive radio network. In: Proceedings of the International Conference on Advances in Electrical Engineering (ICAEE), pp. 161–164

  5. Ali A and Hamouda W 2017 Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Communications Surveys & Tutorials 19: 1277–1304

    Article  Google Scholar 

  6. Awasthi M, Kumar V and Nigam M J 2017 Energy—efficiency techniques in cooperative spectrum sensing: a survey. In: Proceedings of the 3rd International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1–6

  7. Sumi M S and Ganesh R S 2019 Improved EGC method for increasing detection in cognitive radio networks. Comput. Commun. 147: 127–137

    Article  Google Scholar 

  8. Wu H, Zhang T, Chen Y and Liu Y 2019 Multi-bit fusion based energy-efficient collaborative spectrum sensing for cognitive radio network. In: Proceedings of the 19th IEEE International Conference on Communication Technology (ICCT), pp. 776–780

  9. Ma J, Zhao G and Li Y 2008 Soft combination and detection for cooperative spectrum sensing in cognitive radio networks. IEEE Trans. Wireless Commun. 7: 4502–4507

    Article  Google Scholar 

  10. Shen B and Kwak K S 2009 Soft combination schemes for cooperative spectrum sensing in cognitive radio networks. ETRI journal 31: 263–270

    Article  Google Scholar 

  11. Wu S W, Zhu J K, Qiu L and Zhao M 2010 SNR-based weighted cooperative spectrum sensing in cognitive radio networks. J. China Univ. Posts Telecommun. 17: 1–7

    Article  Google Scholar 

  12. Teguig D, Scheers B and Le Nir V 2012 Data fusion schemes for cooperative spectrum sensing in cognitive radio networks. In: Proceedings of the Military Communications and Information Systems Conference (MCC), pp. 1–7

  13. El-Saleh A A, Ismail M, Akbari M, Manesh M R and Zavareh S A R T 2012 Minimizing the detection error of cognitive radio networks using particle swarm optimization. In: Proceedings of the International Conference on Computer and Communication Engineering (ICCCE), pp. 877–881

  14. Teguig D, Scheers B and Le Nir V 2013 Throughput optimization for cooperative spectrum sensing in cognitive radio networks. In: Proceedings of the 7th International Conference on Next Generation Mobile Apps, Services and Technologies, pp. 237–243

  15. Hossain M and El-Saleh A A 2013 Cognitive radio engine model utilizing soft fusion based genetic algorithm for cooperative spectrum optimization. Int. J. Comput. Netw. Commun. IJCNC 5: 23–36

    Article  Google Scholar 

  16. Kaviarasu A and Devapriya S 2014 SNR based adaptive spectrum sensing in cognitive radio networks. Int. J. Eng. Res. Technol. IJERT 3: 1438–1442

    Google Scholar 

  17. Khalid L and Anpalagan A 2014 Reliability-based decision fusion scheme for cooperative spectrum sensing. IET Commun. 8: 2423–2432

    Article  Google Scholar 

  18. Hamza D, Aïssa S and Aniba G 2014 Equal gain combining for cooperative spectrum sensing in cognitive radio networks. IEEE Trans. Wireless Commun. 13: 4334–4345

    Article  Google Scholar 

  19. Guo J, Gu Y and Jing D H 2014 An improved SNR-based cooperative spectrum sensing in cognitive radio networks. Appl. Mech. Mater. Trans Tech Publ. Ltd 631–632: 874–877

    Google Scholar 

  20. Ejaz W, Hattab G, Cherif N, Ibnkahla M, Abdelkefi F and Siala M 2018 Cooperative spectrum sensing with heterogeneous devices: hard combining versus soft combining. IEEE Syst. J. 12: 981–992

    Article  Google Scholar 

  21. Verma P and Singh B 2017 On the decision fusion for cooperative spectrum sensing in cognitive radio networks. Wireless Netw. 23: 2253–2262

    Article  Google Scholar 

  22. Farag H M and Mohamed E M 2017 Soft decision cooperative spectrum sensing with noise uncertainty reduction. Pervasive and Mobile Computing 35: 146–164

    Article  Google Scholar 

  23. Fu Y, He Z and Yang F 2017 A simple quantization-based multibit cooperative spectrum sensing for cognitive radio networks. In: Proceedings of the 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 220–223

  24. Apurva K and Lakshmi P D 2018 To improve the probability of detection in spectrum sensing by using equal gain combining technique. Int. J. Comput. IJC 29: 99–106

    Google Scholar 

  25. Bhatti D M S, Ahmed S, Saeed N and Shaikh B 2018 Efficient error detection in soft data fusion for cooperative spectrum sensing. AEU-Int. J. Electr. Commun. 88: 141–147

    Article  Google Scholar 

  26. Yuan S, Li L and Chigan C 2018 Maximum mean discrepancy based secure fusion strategy for robust cooperative spectrum sensing. In: Proceedings of the IEEE International Conference on Communications (ICC), pp. 1–6

  27. Verma G, Dhage V and Chauhan S S 2018 Analysis of combined data-decision fusion scheme for cognitive radio networks. In: Proceedings of the 2nd International Conference on Inventive Systems and Control (ICISC), pp. 1324–1327

  28. Shah H A, Kwak K S, Sengoku M and Shinoda S 2019 Reliable cooperative spectrum sensing through multi-bit quantization with presence of multiple primary users in cognitive radio networks. In: Proceedings of the 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), pp. 1–2

  29. Kumar A, Saha S and Tiwari K 2019 A double threshold-based cooperative spectrum sensing with novel hard-soft combining over fading channels. IEEE Wireless Commun. Lett. 8: 1154–1158

    Article  Google Scholar 

  30. Awasthi M, Nigam M J and Kumar V 2020 Energy efficiency maximization by optimal fusion rule in frequency-flat-fading environment. AEU-International Journal of Electronics and Communications 113: p.152965

  31. Kusaladharma S and Tellambura C 2017 An overview of cognitive radio networks. Wiley Encyclopedia of Electrical and Electronics Engineering, pp.1–17

  32. Fu Y, Yang F and He Z 2018 A quantization-based multibit data fusion scheme for cooperative spectrum sensing in cognitive radio networks. Sensors 18: 473.

    Article  Google Scholar 

  33. Sumi M S and Ganesh R S 2017 Performance enhancing techniques in cognitive radio networks. In: Proceedings of the IEEE International Conference on Circuits and Systems (ICCS), pp. 172–178

  34. Liang Y C, Zeng Y, Peh E C Y and Hoang A T 2008 Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans. Wireless Commun. 7: 1326–1337

    Article  Google Scholar 

  35. Althunibat S and Granelli F 2014 Energy efficiency analysis of soft and hard cooperative spectrum sensing schemes in cognitive radio networks. In: Proceedings of the IEEE 79th Vehicular Technology Conference (VTC Spring), pp. 1–5

  36. Ansari N and Han T 2017 Green Mobile Networks: A Networking Perspective. Wiley, New York

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    M S Sumi & R S Ganesh

Authors
  1. M S Sumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R S Ganesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M S Sumi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sumi, M.S., Ganesh, R.S. Energy conserving relay assistance for reporting users in cognitive radio networks. Sādhanā 46, 169 (2021). https://doi.org/10.1007/s12046-021-01686-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-021-01686-1

Keywords

Search

Navigation