Call for Paper

CAE solicits original research papers for the July 2021 Edition. Last date of manuscript submission is June 30, 2021.

Read More

SNR Maximization using Fuzzy Rule based System in Relay Assisted Cognitive Radio Networks

Kiran Sultan, Bassam A. Zafar, Waseem Khan, Atta-Ur-Rahman. Published in Fuzzy Systems.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Kiran Sultan, Bassam A. Zafar, Waseem Khan, Atta-Ur-Rahman

Kiran Sultan, Bassam A Zafar, Waseem Khan and Atta-Ur-Rahman. Article: SNR Maximization using Fuzzy Rule based System in Relay Assisted Cognitive Radio Networks. Communications on Applied Electronics 4(4):42-48, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Kiran Sultan and Bassam A. Zafar and Waseem Khan and Atta-Ur-Rahman},
	title = {Article: SNR Maximization using Fuzzy Rule based System in Relay Assisted Cognitive Radio Networks},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {4},
	pages = {42-48},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Performance enhancement of secondary communication, while adhereing to the interference constraint of the primary network in an underlay sepctrum sharing environemnt, is an active area of research. In this paper, we propose a Fuzzy Rule Based System (FRBS) assisted relay selection and transmit power allocation (RSTPA) technique that provides the secondary users the ability to coexist with the primary users in an energy-constrainted dual-hop Cognitive Radio Network. In this proposal, FRBS selects the optimal combination of relays aiming to maximize the signal-to-noise ratio (SNR) received at the destination, while guaranteeing that interference threshold of the primary network is not exceeded. The proposed scheme has been investigated for well-defined range of certain parameters. Simulation results prove that FRBS can accurately select the best combination of relays and maximize the SNR. Its performance in terms of end-to-end SNR has been compared with another scheme in the literature for multiple relay selection.


  1. Nikjah, R. , Beaulieu, N.C.. 2009. Exact Capacity Analysis of Rate Adaptive Power Non adaptive Multibranch Multihop Decode-and-Forward Relaying Networks. GLOBECOM IEEE. 1-8.
  2. Laneman, J.N. ; Wornell, G.W.. 2000. Energy Efficient Antenna Sharing and Relaying for Wireless Networks. WCNC IEEE. 7-12.
  3. Im S., Kim W, Kang Y., Lee H. 2010. Joint Power and Admission Control for Underlay Spectrum Sharing in Cognitive Radio Networks. ATC IEEE. 56-6.
  4. Sultan K., Qureshi I.M., Zubair M. 2012. SNR Maximization through Relay Selection and Power Allocation for Non-Regenerative Cognitive Radio Networks. INMIC IEEE. 361-364.
  5. Gomaa A., Mokhtar M., Al-Dhahir N. 2012. Amplify-and-Forward Relaying under I/Q Imbalance. GLOBECOM IEEE. 4671-4676.
  6. Sun C., Letaief K. B. 2008. User Cooperation in Heterogeneous Cognitive Radio Networks with Interference Reduction. ICC IEEE, 3193-3197.
  7. M. Naeem, D.C. Lee, U. Pareek, 2010. An Efficient Multiple Relay Selection Scheme for Cognitive Radio Systems”, Proceedings of IEEE International Conference on Communications, 1-5.
  8. M.A. Manna, G. CHEN, J.A. CHAMBERS. 2014. Outage Probability Analysis of Cognitive Relay Network with Four Relay Selection and End-To-End Performance with Modified Quasi-Orthogonal Space–Time Coding”, IET Commun., vol. 8, no. 2, 233-241.
  9. G. Chen, O. Alnatouh, J. Chambers., 2013. Outage Probability Analysis for A Cognitive Amplify-And-Forward Relay Network With Single and Multi-Relay Selection”, IET Commun., vol. 7, no. 17, 1974-1981.
  10. Atta-ur-Rahman, Qureshi I.M., Malik A.N. 2012. A Fuzzy Rule Base Assisted Adaptive Coding and Modulation Scheme for OFDM Systems. J. Basic Appl. Sci. Res. Vol. 2(5), 4843-4853.
  11. Liu W., Lv T., Gao L., Wang W., Liu B. 2009. A Novel Cooperative Spectrum Sensing Scheme Based on Fuzzy Integral Theory in Cognitive Radio Networks. WiCom IEEE. 1-4.
  12. Zhang H., Wang X. 2011. A Fuzzy Decision Scheme for Cooperative Spectrum Sensing in Cognitive Radio. VTC IEEE. 1-4.
  13. Ejaz W. , ul Hasan, N. , Aslam, S. ,  Kim H. S. 2011. Fuzzy Logic Based Spectrum Sensing for Cognitive Radio Networks”, NGMAST IEEE. 185-189.
  14. Aryal S. R., Dhungana H., Paudyal K. 2012. Novel Approach for Interference Management in Cognitive Radio. AH-ICI IEEE. 1-5.
  15. Le H.S.T., Liang Q. 2007. An Efficient Power Control Scheme for Cognitive Radios. WCNC IEEE. 2559-2563.
  16. Tabakovic Z., Grgic S., Grgic M. 2009. Fuzzy Logic Power Control in Cognitive Radio. IWSSIP IEEE. 1-5.
  17. Mustafa, W. , Yu J.S., Rakus-Andersson, E. , Mohammed, A. ,  Kulesza, W.J. 2010. Fuzzy-based Opportunistic Power Control Strategy in Cognitive Radio Networks. ISABEL IEEE. 1-9.
  18. Dey A., Biswas S., Panda S. 2011. A New Fuzzy Rule Based Power Management Scheme for Spectrum Sharing in Cognitive Radio. ICCIA IEEE. 1-4.
  19. Xu J., Zhang H., Yuan D., Jin Q., Wang C. 2011. Novel Multiple Relay Selection Schemes in Two-Hop Cognitive Relay Networks. CMC IEEE. 307-310.
  20. Hui H., Zhu S. , Lv G. 2010. Relay Selection for Lifetime Extension in Amplify-and-Forward Cooperative Networks. ICC IEEE. 1-5.
  21. Amarasuriya G., Ardakani M., Tellambura C. 2010. Output-Threshold Multiple-Relay-Selection Scheme for Cooperative Wireless Networks. IEEE Transactions on Vehicular Technology. Vol. 59(6). 3091-3097.


Cognitive Radio Network, Underlay Spectrum Sharing, Cooperative Communication, Amplify-and-Forward, Fuzzy Rule Based System.