Abstract
With rapid evolution in wireless devices increases the demand for radio spectrum. To solve spectrum underutilization problem cognitive radio technology is introduced. Cognitive radio technology is next generation technology which allows non-licensed user to use electromagnetic spectrum without interfering licensed user. To use white space in radio spectrum one should sense the spectrum perfectly. Once sensing is done, the distribution of the spectrum among the secondary user is also challenging task. Optimizing is the process to find best solution among the available solutions. Radio environment is random in nature. Due to fast convergence property of the genetic algorithm can use to find optimal solution for spectrum allocation problem to maximizing spectral utilization. Problem is modeled as Multi Objective Problem (MOP), considering that function as fitness function and evaluating the best allocation among all. Firstly defining target objective function that is minimizing Bit Error Rate (BER), maximizing throughput and minimizing power, then using aggregate sum approach, it converts all single objective function into one MOP. Than mathematically applying the fitness function in software so we get graphical representation. We have check convergence of algorithm first. Than we simulate result for single channel and multichannel performance. By observation of graphical parameter we have simulate results for real scenario and get optimum parameter for given situation.
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References
FCC: FCC. 03-322-notice of proposed rule making and order. Technical report, Federal Communications Commission, 30 December 2003
Muchandi, N., Khanai, R.: Cognitive radio spectrum sensing: a survey. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE (2016)
Ghosh, G., Das, P., Chatterjee, S.: Simulation and analysis of cognitive radio system using Matlab. Int. J. Next-Gener. Netw. 6(2) (2014). 31. K. Elissa, “Title of paper if known,” unpublished
Newman, T.R., et al.: Population adaptation for genetic algorithm-based cognitive radios. Mob. Netw. Appl. 13(5), 442–451 (2008)
Varade, P.S., Ravinder, Y.: Optimal spectrum allocation in cognitive radio using genetic algorithm. In: 2014 Annual IEEE India Conference (INDICON). IEEE (2014)
Hamza, A.S., Elghoneimy, M.M.: On the effectiveness of using genetic algorithm for spectrum allocation in cognitive radio networks. In: 2010 High-Capacity Optical Networks and Enabling Technologies (HONET). IEEE (2010)
Pradhan, P.M., Panda, G.: Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making. Swarm Evol. Comput. 7, 7–20 (2012)
Pradhan, P.M., Panda, G.: Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: a survey. Ad Hoc Netw. 17, 129–146 (2014)
El-Saleh, A.A., Ismail, M., Ali, M.: Pragmatic trellis coded modulation for adaptive multi-objective genetic algorithm-based cognitive radio systems. In: 2010 16th Asia-Pacific Conference on Communications (APCC). IEEE (2010)
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Patel, N., Pathak, K., Patel, R. (2018). Optimize Spectrum Allocation in Cognitive Radio Network. In: Patel, Z., Gupta, S. (eds) Future Internet Technologies and Trends. ICFITT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 220. Springer, Cham. https://doi.org/10.1007/978-3-319-73712-6_21
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DOI: https://doi.org/10.1007/978-3-319-73712-6_21
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