• Journal of Advanced Navigation Technology
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• v.12 no.1
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• pp.22-27
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• 2008

This paper presents adaptive-MMOSPRED method for prediction of resource demands requested by multimedia calls, and shows the performance of the call admission control based on proposed resource prediction method in multimedia wireless networks. The proposed method determines (I-CDP) random variables of the standard normal distribution by using LMS algorithm that minimize errors of prediction in resource demands, while parameters in an existing method are constant all through the prediction time. Our simulation results show that prediction error in adaptive-MMOSPRED method is much smaller than in fixed-MMOSPRED method. Also we can see via simulation the CAC performance based on the proposed method improves the new call blocking performance compared with the existing method under the desired handoff dropping probability.

• Journal of Advanced Navigation Technology
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• v.16 no.5
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• pp.794-800
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• 2012

In this paper, we propose a scheme for improving not only the utilization of frequency bands in the guard channel scheme but also the dropping rate of cognitive radio user in the wireless cognitive radio-based cellular network. The proposed scheme enables cognitive radio users to utilize the guard channel for servicing only handoff calls in normal times, but cognitive radio users must vacate the frequency channel when handoff call appearing. At this time our scheme ensures their seamless services for cognitive radio users, by predicting handoff call's appearance by MMOSPRED (Multi-Media One Step Prediction) method and then reserving the demanded channels for spectrum handoff calls. Our simulations show that our scheme performs better than other schemes; GCS(Guard Channel Scheme) and a scheme without prediction in terms of cognitive users call's dropping rate and resource utilization efficiency.

• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.372-377
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• 2009

This paper evaluates the performance of three methods for predicting resources requested by mobile's calls and a call admission algorithm based on these predicting methods respectively in wireless networks. The first method is based on Wiener prediction model and the second method is based on the MMOSPRED algorithm and the third method is based on the neural network. The proposed call admission algorithm is based on prioritized handoff call in resource allocation. The resources for future handoff calls are therefore predicted and reserved in advance, and then new calls are admitted as long as the remaining resources are sufficient. We compare their performances in terms of prediction error, new call blocking and handoff dropping probabilities by simulation. Results show that the CAC based on Wiener prediction model performs favorably due to exact resources prediction.