Abstract
The Cognitive Cellular Network (CCN) is the key to complete the requirement of the cellular user request along with the improvement in channel allocation. CCN consist of cellular user as a primary and cognitive as secondary user in which the cognitive user occupies in the cellular band without causing interference. To check and improvise the network capacity of the cellular network, the Bacterial colony optimization (BCO) algorithm is considered. The BCO algorithm is developed from the lifecycle of E-coli bacteria. The signal to interference ratio (SINR) obtained with proposed BCO is found to be better as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC).
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References
Marshall, P.: Scalability, Density, and Decision Making in Cognitive Wireless Networks. Cambridge University Press, Cambridge (2013)
Rappaport, T.S.: Wireless Communications. Prentice-Hall, Upper Saddle River (2002)
Gardellin, V., Das, S.K., Lenzini, L.: Self-coexistence in cellular cognitive radio networks based on the IEEE 802.22 standard. IEEE Wirel. Commun. 20, 52–59 (2013)
Ohatkar, S.N., Bormane, D.S.: Channel allocation technique with genetic algorithm for interference reduction in cellular network. In: Annual IEEE India Conference (INDICON), pp. 1–6 (2015)
Vargas, A.M., Andrade, A.G.: Deployment analysis and optimization of heterogeneous networks under the spectrum underlay strategy. EURASIP J. Wirel. Commun. Netw. 55, 1–15 (2015)
Niu, B., Xie, T., Bi, Y., Liu, J.: Bacterial colony optimization for integrated yard truck scheduling and storage allocation problem. In: Huang, D.-S., Han, K., Gromiha, M. (eds.) ICIC 2014. LNCS, vol. 8590, pp. 431–437. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09330-7_50
Cosio-León, M., Martınez-Vargas, A., Gutierrez, E.: An experimental study of parameter selection in particle swarm optimization using an automated methodology. Res. Comput. Sci., p. 920
Ohatkar, S., Gunjkar, Y.: ABC and TLBO technique for evaluating data rate in wireless network. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds.) SmartCom 2016. CCIS, vol. 628, pp. 390–399. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-3433-6_47
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Ohatkar, S., Tupe, K. (2018). Bacterial Colony Optimization for Data Rate Evaluation in Cognitive Cellular Network. In: Deshpande, A., et al. Smart Trends in Information Technology and Computer Communications. SmartCom 2017. Communications in Computer and Information Science, vol 876. Springer, Singapore. https://doi.org/10.1007/978-981-13-1423-0_31
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DOI: https://doi.org/10.1007/978-981-13-1423-0_31
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