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Annals of Computer Science and Information Systems, Volume 10

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

Nature Inspired Techniques for Interference Management in Femtocells: A Survey

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DOI: http://dx.doi.org/10.15439/2017R115

Citation: Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, Vijender Kumar Solanki, Vijay Bhasker Semwal, Rubén González Crespo, Vishwanath Bijalwan (eds). ACSIS, Vol. 10, pages 157–160 ()

Full text

Abstract. In the wireless communication system the transmitter and receiver close to each other to improve the data rates and capacity. Therefore, the wireless networks are more popular than the traditional wired services. In the wireless networks, to cover cells the low power nodes such as macrocells, picocells, femtocells base stations (BSs) deployed to improve the indoor coverage. The femtocell base station reduces operators operational cost, maintainance and infrastructure. At the time of femtocell deployment, the femtocell base station deal with a number of technical challenges, among those all the interference management is more important. In femtocell network, one femtocell creates the interference to its neighboring femtocells.To deal with interference management challenge number of researchers have suggested different types of solutions. The survey shows that nature inspired metaheuristic algorithm has the powerful impact on interference cancellation and avoidance.This survey paper focuses on bat algorithm for the resource allocation problem in a femtocell

References

  1. http://www.qualcomm.com/solutions/wirelessnetworks/technologies/femtocells.
  2. 3GPP, “Requirement for Envolved UTRA(E-UTRA) and Evolved UTRAN (EUTRAN),” TR 25.913, 3rd Generation Partnership Project(3GPP), Mar 2006.
  3. L. T. W. Ho and H. Claussen, “Effects of user -deployed, co-channel femtocells on the cell drop probability in a residential scenario,” IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communication, pp.1-5,3-7 Sept.2007.
  4. R. Baines, “The nedd for WiMAX picocells and femtocells,” WiMAX London, pp. 1-36, 25-26 April 2007.
  5. H. Su, L. Kuang and J. Lu, “Interference avoidance in OFDMA based femtocell network,” IEEE youth Conference on Information, Computing and telecommunication, pp. 126-129, 20-21 Sept. 2009.
  6. M. Yavuz, F. Meshkati, S. Nanda, A. Pokharuyal,N. Johnson, B. Roghothaman, and A. Richardson,”Interference Management and performance analysis of UMTS/HSPA femtocells,” IEEE commun. Mag. , vol. 47, no.9, pp. 102-109, sept. 2009.
  7. K. han, Y. choi, D. Kim, M. Na, S. choi, and K. Han, “Optimization of femtocell network configuration under interference constraints,”7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pp. 1-7, 23-27 Jun.2009.
  8. A. K. Elhakeen, R. D. Girolamo, I.B. Bdira, and M. Talla, “Delay and throughput characteristics of cdma, tdma and hybrid networks for multipath faded data transmission channels,” IEEE J. Se. Areas Commun., vol 12, no.4, pp.622-637, May 1994
  9. T. Kim and T. Lee, “Throughput enhancement if macro and femto networks by frequency reuse and pilot sensing,” in performance, computing and communication Conference, IPCCC 2008. IEEE International, pp. 390-394, IEEE, 2008
  10. P. Frenger, P. Orten and T. Ottosson, “Code spread cdma with interference cancellation,” IEEE J. Sel. Areas Commun., vol. 17, no. 12, pp. 2090-2095, 1999.
  11. J. G. Andrews, “Interference Cancellation for cellular system: A contemporary overview,” IEEE Wireless Commun., vol 2, no. 3, pp. 19-29, April, 2005.
  12. M. Honig, U. Madhow, and S. Verdu, “Blind adaptive multiuser detection,” IEEE Trans. Inf. Theory, vol. 41, no. 4, pp. 944-960, July 1995.
  13. R. Estrada, H. Otrol, and Z. Dziong, Resource allocation model based on particle swarm optimization for OFDMA macro-femtocell networks, IEEE ANTS,2013.
  14. A. Rezaee Jordehi, Chaotic bat swarm optimization,Elsever,Applied Soft Computing, 2014.
  15. Motea Al-omari, Abd Rahman Ramli, A. Sali and Raja Syamsul Azmir, A Femtocell cross-tier interference mitigation technique in OFDMA_LTE system: A cuckoo search based approach, Indian Journal of Science and Technology, Vol9(2), http://dx.doi.org/10.17485/ijst/2016/v9i2/80490, January 2016.
  16. J. Zhou, X. She, L. Chen, and H. Otsuka, Qos guaranteed radio resource allocation scheme using genetic algorithm for OFDMA. In Communications and Networking in china (CHINACOM), 2011 6th International ICST Conference on, pages 594-599. IEEE, 2011.
  17. Selim Yılmaza, Ecir U. Kucuksille, A new modification approach on bat algorithm for solving optimization problems, Applied Soft Computing 28 (2015) 259-275, Elsevier, 2014.
  18. Ahmad Rezaee Jordehi, Chaotic bat swarm optimization, Articke in apllied soft computing. October 2014.
  19. Xin She Yang, A new metaheuristic bat inspired algorithm, April 2010.
  20. Rebeca Estrada, Hali Otrok et el. Resource allocation model based on particle swarm optimization for OFDMA macro femtocell networks, IEEE ANTS,2013.
  21. Hanaa Marshod, Hadi Otrok, et el., Resource allocation in macrocell femtocell network using genetice algorithm, IEEE 8th International conference on wireless and mobile computing, networking and communication (WiMob), 2008.
  22. Zhuo Li, Song Guo, et. el., A particle swarm optimization algorithm for resource allocation in femtocell networks, IEEE wireless communication and networking conference: MAC & Cross-layer design, 2012.
  23. Iztok Fister, Xin She Yang, et. el., A hybrid bat algorithm, 5 june 2013.
  24. Vu Truong Vu, A coparison of particle swarm optimization and differential evolution, International Journal on soft computing vol 3, August 2012.
  25. Xin She Yang, Bat algorithm for multiobjective optimization, 29 march 2012.
  26. D. Liu, H. Zheng, and X. Wen, The sub-channel allocation algorithm in femtocell networks based on ant colony optimization, IEEE, 2013.