Energy Efficient Collaborative Spectrum Sensing in Cognitive Radio Networks

Mr.S.Esakki Rajavel, Francis Xavier Engineering College, Tamilnadu,,India; Mr.B.Pradheep T Rajan ,Francis Xavier Engineering College, Tamilnadu,,India; E.Edinda Christy ,Francis Xavier Engineering College, Tamilnadu,,India

EE-CSS Protocol T-CSS, Data Fusion Techniques

Depending on trust management, an energy efficient collaborative spectrum sensing (EE-CSS) protocol is put forward. In contrast to the traditional collaborative spectrum sensing (T-CSS), here we are attaining energy efficiency by limiting the overall count of sensing reports exchanged between the honest secondary users (HSUs) and the secondary user base station (SUBs). In EE-CSS, it is concluded that the least total count of sensing reports needed to fulfill a target global false alarm (FA) and missed detection (MD) probabilities in T-CSS is more. We are calculating steady-state average SU trust value and total count of SU sensing reports transmitted in both T-CSS and EE-CSS. Derivations are made for the global FA and detection probabilities Qf and Qd for a data fusion method. The effect of link outages on Qf and Qd are also examined. The output tells that the energy consumption in T-CSS is comparatively higher than in EE-CSS for long range communications where the transit energy is dominant.
    [1] Chen. H, Jin. X, and Xie. L, “Reputation-based collaborative spectrum sensing algorithm in cognitive radio networks,” in Proc. IEEE Int. Symp. PIMRC, 2009, pp. 582–587. [2] Chen. R, Park. J-M, and Bian. K, “Robust distributed spectrum sensing in cognitive radio networks,” in Proc. IEEE INFOCOM, 2008, pp. 1876–1884. [3] Chen. H, Wu. H, Zhou. X, and Gao. C, “Agent-based trust model in wireless sensor networks,” in Proc. ACIS Int. Conf. SNPD, 2007, pp. 119–124. [4] Ghasemi. A and Sousa. E, “Collaborative spectrum sensing for opportunistic access in fading environments,” in Proc. IEEE DySPAN, 2005, pp. 131–136. [5] Ghasemi. A and Sousa. E, “Opportunistic spectrum access in fading channels through collaborative sensing,” J. Commun., vol. 2, no. 2, pp. 71–82, Mar. 2007. [6] S.Esakki Rajavel, C.Jenita Blesslin, “Energetic Spectrum Sensing For Cognitive Radio Enabled Remote State Estimation Over Wireless Channels”, International Journal of Advanced Research Trends in Engineering and Technology (IJARTET), Vol. 3, Special Issue 19, April 2016 (12 – 15). [7] Hillenbrand. J, Weiss. T, and Jondral. F, “Calculation of detection and false alarm probabilities in spectrum pooling systems,” IEEE Commun. Lett., vol. 9, no. 4, pp. 349–351, Apr. 2005. [8] Hong. L, Ma. J, Xu. F, Li. S, and Zhou. Z, “Optimization of collaborative spectrum sensing for cognitive radio,” in Proc. IEEE ICNSC, 2008, pp. 1730–1733. [9] Li.H and Han. Z, “Catch me if you can: An abnormality detection approach for collaborative spectrum sensing in cognitive radio networks,” IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3554–3565,Nov. 2010. [10] Mishra. S, Sahai. A, and R. Brodersen, “Cooperative sensing among cognitive radios,” in Proc. IEEE ICC, 2006, pp. 1658–1663. [11] Haykin.S ,“Cognitive radio: Brain-empowered wireless communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005.
Paper ID: GRDJEV02I010027
Published in: Volume : 2, Issue : 1
Publication Date: 2017-01-01
Page(s): 26 - 29