• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2A
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• pp.26-33
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• 2005

Exact prediction of resource demands for future calls enhances the efficiency of the limited resource utilization in resource reservation methods for potential calls in wireless IP networks. In this paper, we propose a LMS-Wiener resource(bandwidth) prediction for future handoff calls, and then an the proposed method is compared with an existing Wiener-based method in terms of prediction error through our simulations. In our simulations, we assume that handoff call arrivals follow a non-Poisson process and each handoff call has an non-exponentially distributed channel holdingtime in the cell, considering that handoff call arrival pattern is not Poisson distribution but non-Poisson for long periods of time in wireless picocellular IP networks. Simulation results show that the prediction error in the proposed method converges to the lower value while in an existing method increase as time is passed. Therefore we may conclude that the proposed method improves the efficiency of resource utilization by more exactly predicting resource demands for future handoff calls than an existing method.

• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.322-327
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• 2019

In this paper, we propose first a scheme of grouping spectrum holes that are created in the multiple channels of primary users, and then by using the scheme we enhance quality of service (QoS) of wideband cognitive radio users in cellular cognitive radio networks. In our scheme, spectrum holes created in each primary channel are predicted by Wiener prediction process, and then the predicted spectrum holes happened in the same time are grouped into a group. The wideband cognitive radio users explore the group of spectrum holes to improve their QoS. Simulation results show that their handoff calls dropping rate and initial calls blocking rate are significantly reduced in our scheme, compared to those in the single primary channel.

• Journal of the Korea Society for Simulation
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• v.17 no.3
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• pp.19-26
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• 2008

In this paper, we propose a dynamic prediction algorithm to predict the bond price using actual data set of treasure note (T-Note). The proposed algorithm is based on term structure model of the interest rates, which takes place in various financial modelling, such as the standard Gaussian Wiener process. To obtain cumulative distribution functions (CDFs) of actual data for the interest rate measurement used, we use the natural cubic spline (NCS) method, which is generally used as numerical methods for interpolation. Then we also use the random number generation scheme (RNGS) to calculate the pricing of bond through the obtained CDF. In empirical computer simulations, we show that the lower values of precision in the proposed prediction algorithm corresponds to sharper estimates. It is very reasonable on prediction.