Traffic modelling and forecasting using genetic algorithms for next-generation cognitive radio applications



This article presents a genetic-algorithm-based prediction model for forecasting traffic demands of next-generation wireless networks that are expected to be chaotic in nature. The model approximates the best-fit mathematical equation that generates a given time series using a genetic algorithm. It estimates future traffic in wireless networks using the most recent traffic data points collected from the actual network. Such estimations will be beneficial for network operators helping to manage and optimise the limited radio resources efficiently and eventually to facilitate cognitive radio applications. The new model is compared with conventional regressions analysis and exponential smoothing models, and it has been found that the genetic algorithm model successfully recovers the underlying mathematical expression describing chaotic time series in less than 200 generations and the predictions achieved are by far better than those of regression and exponential smoothing models. The model also offers benefits for in cognitive communication systems with their intrinsic learning capabilities and distributed access decisions.


Genetic algorithm Wireless network Cognitive radio application 



This work was performed in project E3 which has received research funding from the Community's Seventh Framework programme. This paper reflects only the authors' views and the community is not liable for any use that may be made of the information contained therein. The contributions of colleagues from E3 consortium are hereby acknowledged.


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Copyright information

© Institut TELECOM and Springer-Verlag 2009

Authors and Affiliations

  1. 1.Centre for Communication Systems ResearchUniversity of SurreyGuildfordUK

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