Abstract
It is an important issue to allocate appropriate resources to mobile calls for wireless cellular networks owe to scarce wireless spectrums. The call admission control (CAC) will maintain better performance metrics of mobile call such as call dropping probability (CDP) and call blocking probability (CBP) if the future utilization of wireless spectrums can be predicted and provided to the decision of CAC. Therefore, a prediction mechanism which can predict most information such as system utilization is proposed in this paper. The techniques of data mining and pattern matching which adopts gradient to fuzz time series data for representations of chain code are applied to mining a possible repetitive pattern. Our proposed prediction mechanism can provide prediction information in advance whether the repetitive time series pattern of information exists or not. Furthermore, an update of confident level will be conducted continuously for performing each prediction in the proposed scheme. Our proposed mechanism is developed and tested with four cases which can be regarded as using scenarios of wireless cellular network. The experimental results show that the proposed scheme can capture repetitive time series patterns and perform following predictions with these repetitive time series patterns. Besides, the required storage is less than traditional schemes and lower computation power is required for the proposed scheme.
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Wu, CF., Chang, YT., Lo, CY., Zhuang, HS. (2010). A Novel Prediction Mechanism with Modified Data Mining Technique for Call Admission Control in Wireless Cellular Network. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_1
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DOI: https://doi.org/10.1007/978-3-642-12990-2_1
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