Abstract
Some load balancing algorithms in heterogeneous wireless networks can not consider the problems arising from the admission control of new service and service transfer of heavy load networks. To solve these problems, we propose a load balancing algorithm based on neural networks. This algorithm is used to conduct prediction through network load rate and achieve the network admission of new service by combining an admission control optimization algorithm. Moreover, by analyzing network performance, some services of heavy load network are transferred to overlay light load network. The simulation results indicate that our algorithm can well realize the load balancing of heterogeneous wireless network and provide high resource utilization.
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References
Cao, J., Zhang, C.: Heterogeneous Wireless Networks. In: Seamless and Secure Communications over Heterogeneous Wireless Networks, pp. 9–26. Springer, New York (2014)
Steele, R., Nofal, M.: Teletraffic performance of microcellular personal communication networks, Communications, Speech and Vision. Communications, Speech and Vision, IEE Proceedings I. IET 139(4), 448–461 (2008)
Xiao, Z., Zhang, Y., Lv, Z.: Study of Self-optimizing Load Balancing in LTE-Advanced Networks. In: Tan, H. (ed.) Knowledge Discovery and Data Mining. AISC, vol. 135, pp. 217–222. Springer, Heidelberg (2012)
Skehill, R., Barry, M., Kent, W., et al.: The common RRM approach to admission control for converged heterogeneous wireless networks. IEEE Wireless Communications 14(2), 48–56 (2007)
Song, W., Zhuang, W.H., Cheng, Y.: Load balancing for cellular/ WLAN integrated networks. IEEE Network 21(1), 27–33 (2007)
Rong, C., Xiao-Yu, D., Jie, M., et al.: An optimal IASA load balancing scheme in heterogeneous wireless networks. In: Communications and Networking in China (CHINACOM), pp. 714–719 (2011)
Jin, L., Zhang, H., Yang, L.X., Zhu, H.B.: A novel adaptive vertical handoff algorithm based on UMTS and WLAN. Journal of Nanjing University of Posts and Telecommunications (Natural Science) 33(4), 13–18 (2013)
Jiao, Y., Yi, K.C., Ma, M.D., Ma, Y.H., Dong, X.: QoS-aware load-balancing algorithm for heterogeneous wireless networks. Journal of Jilin University (Engineering and Technology Edition) 43(3), 794–800 (2013)
Johnson, S.B., Nath, S., Velmurugan, T.: An Optimized Algorithm for Vertical Handoff in Heterogenenous Wireless Networks. In: Proceeding of 2013 IEEE Conference on Information and Communication Technologies (ICT 2013), pp. 1206–1210 (2013)
Yongjing, Z., Kui, Z., Cheng, C., et al.: An Adaptive Threshold Load Balancing Scheme for the End-to-End Reconfigurable System. Wireless Personal Communication 46, 47–65 (2008)
Sheng, J., Tang, L.R., Hao, J.H.: Hybrid Load Balancing Algorithm Based on Service Transformation and Admission Control in Heterogeneous Wireless Networks. Acta Electronica Sinica 41(2), 321–328 (2013)
Sheng, J., Tang, L.R.: A triangle module operator and fuzzy logic based handoff algorithm for heterogeneous wireless networks. In: ICCT 2010, Nanjing, China, pp. 488–491 (2010)
Nasri, R., Altman, Z.: Handover adaptation for dynamic load balancing in 3GPP long term evolution systems. In: Proceeding of MoMM 2007, pp. 145–153 (2007)
Badia, L., Zorzi, M., Gazzini, A.: A model for threshold comparison call admission control in third generation cellular systems. In: International Conference on Communications, ICC 2003, pp. 1664–1668. IEEE (2003)
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© 2015 Springer International Publishing Switzerland
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Song, X., Wu, L., Ren, X., Gao, J. (2015). Load Balancing Algorithm Based on Neural Network in Heterogeneous Wireless Networks. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_51
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DOI: https://doi.org/10.1007/978-3-319-25393-0_51
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