Cognitive femtocell has emerged as a promising technique for indoor users in wireless communication systems since they opt the cognitive and self-configuration capabilities. Spectrum sensing is a non-trivial approach in cognitive radio networks. A number of methods have been used for spectrum sensing in femtocells with a cognitive engine. In this work, the authors have proposed a near field source localisation technique through which the cognitive femtocell will be able to detect the active femtocells in the licensed band and will get additional information including the range, amplitude, angle and operating frequency of a certain femtocell.

References

1. 1)
  - 13. Srinivas, N.: ‘Multiobjective optimization using nondominated sorting in genetic algorithms’, Evol. Comput., 2007, 2, (3), pp. 221–248.
2. 2)
  - 19. Grosicki, E., Abed-Meraim, K., Hua, Y.: ‘A weighted linear prediction method for near-field source localization’, IEEE Trans. Signal Process., 2005, 53, (10), pp. 3651–3660.
3. 3)
  - 17. Hassan, R., Cohanim, B., de Weck, O.: ‘A comparison of particle swarm optimization and the genetic algorithm’. 46th Structural Dynamics and Materials Conf., April 2005, Austin, TX.
4. 4)
  - 1. Chandrasekhar, V., Andrews, J., Gatherer, A.: ‘Femtocell networks: a survey’, IEEE Commun. Mag., 2008, 46, (9), pp. 59–67.
5. 5)
  - 21. Salman, A., Qureshi, I.M., Sultan, K., et al: ‘Joint spectrum sensing for detection of primary users using cognitive relays with evolutionary computing’, IET Commun., 2015, 9, (13), pp. 1643–1648.
6. 6)
  - 5. Yucek, T., Arslan, H.: ‘A survey of spectrum sensing algorithms for cognitive radio applications’, IEEE Communications surveys & tutorials, 2009, 11, (1), pp. 116–130.
7. 7)
  - 22. Khwaja, A.S., Naeem, M., Anpalagan, A.: ‘Pattern-search-based nonconvex cooperative sensing in multiband cognitive radio systems’, IEEE Syst. J., 2016, 10, (2), pp. 580–591.
8. 8)
  - 11. Proakis, J.G.: ‘Digital communications’ (McGraw- Hill, 2001, 4th edn.).
9. 9)
  - 16. Elhachmi, J., Guennoun, Z.: ‘Cognitive radio spectrum allocation using genetic algorithm’, EURASIP J. Wirel. Commun. Netw., 2016, 2016, (133), pp. 1–11.
10. 10)
  - 8. Song, J., Feng, Z., Zhang, P., et al: ‘Spectrum sensing in cognitive radios based on enhanced energy detector’, IET Commun., 2012, 6, (8), pp. 805–809.
11. 11)
  - 15. Mishra, P., Dewangan, N.: ‘Survey on optimization methods for spectrum sensing in Cognitive radio networks’, Int. J. New Technol. Res., 2015, 1, (6), pp. 23–28.
12. 12)
  - 12. Yucek, T., Arslan, H.: ‘Spectrum characterization for opportunistic cognitive radio systems’. Proc. IEEE Military Communication Conf., October 2006, pp. 1–6.
13. 13)
  - 2. ‘Interference Management in UMTS femtocell’, Femto forum, December 2008 http://www.scribd.com/doc/474108779/Interference-Management-in-UMTS-Femtocells.
14. 14)
  - 18. Liang, J., Liu, D.: ‘Passive localization of mixed near-field and far-field sources using two-stage MUSIC algorithm’, IEEE Trans. Signal Process., 2010, 58, (1), pp. 108–120.
15. 15)
  - 20. Zaman, F., Khan, S.U., Ashraf, K., et al: ‘An application of hybrid differential evolution to 3-D near field source localization’. IBCAST, January 2014, pp. 474–477.
16. 16)
  - 14. Hauris, J.F.: ‘Genetic algorithm optimization in a cognitive radio for autonomous vehicle communications’. Int. Symp. on Computational Intelligence in Robotics and Automation, Jacksonville, FL, 2007, pp. 427–431.
17. 17)
  - 4. Mitola III, J.: ‘Cognitive radio: An integrated agent architecture for software defined radio’. PhD Thesis, KTH Royal Institute of Technology, Sweden, May 2000.
18. 18)
  - 10. Khambekar, N., Dong, L., Chaudhary, V.: ‘Utilizing OFDM guard interval for spectrum sensing’. Proc. IEEE Wireless Communications and Networking Conf., March 2007, pp. 38–42.
19. 19)
  - 7. Xuping, Z., Jianguo, P.: ‘Energy-detection based spectrum sensing for cognitive radio’. IET Conf. on Wireless, Mobile and Sensor Networks, December 2007, pp. 944–947.
20. 20)
  - 6. Urkowitz, H.: ‘Energy Detection of unknown deterministic signals’, Proc. IEEE, 1967, 55, pp. 523–531.
21. 21)
  - 9. Tang, H.: ‘Some physical layer issues of wide-band cognitive radio systems’. Proc. IEEE Int. Symp. on New Frontiers in Dynamic Spectrum Access Networks, November 2005, pp. 151–159.
22. 22)
  - 3. Chen, J., Rauber, P., Singh, D., et al: ‘Femtocells-Architecture and Network Aspects’, Qualcomm, January 2010 http://www.qualcomm.com/commom/documents/white-papers/Femto-Overview-Rev-C.pdf.

Spectrum sensing in cognitive femtocell network based on near-field source localisation using genetic algorithm

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