在一個多異質網路的整合系統中,
如何提出一有效率的網路選擇機制 (Network Selection Scheme)使行動終端 (Mobile Terminal, MT)於需要的時候交遞至合適的網路已成為次一世代行動通訊網路發展的重要議題。
在本論文中,我們提出一預測式網路選擇機制 (Predictive Network Selection Scheme, PNSS),
PNSS機制使用效用函數 (Utility Function),
並以網路的頻寬 (Bandwidth)、訊號強度 (Received Signal Strength, RSS)、連線成本 (Connection Cost)及延遲 (Delay)做為效用函數的輸入參數,
使行動終端可計算效用值 (Utility Value)來選取交遞目標網路。
除了以目前網路的參數值做為效用函數的輸入參數,我們還額外考量網路於下一個時間的可能參數值,
包括以馬可夫鏈(Markov Chain)及頻寬參數之狀態轉移機率 (State Transition Probability)所計算之下一個時間點的頻寬期望值及利用線性迴歸 (Linear Regression)與指數移動平均 (Exponential Moving Average, EMA)所計算之下一個時間點訊號強度的預測值,
於是使效用函數能同時反映目前網路的狀態及未來可能的狀態,
讓行動終端選取目前狀態較佳且預期於未來狀態會變佳的網路,以減少行動終端進行交遞的次數。
此外,於PNSS機制中我們也提出一網路預選策略 (Network Pre-Selection Strategy)以依據預先設定的速度與頻寬需求門檻值將行動終端進行分類,
使不同類型的行動終端有不同選取網路的優先順序,以進一步提升系統效能。最後,
模擬結果顯示PNSS機制相較於文獻上之相近機制確實可進一步降低阻斷機率 (Blocking Probability)、平均交遞次數、平均連線成本及平均延遲。

In a system that integrates heterogeneous networks,
how to design an efficient network selection scheme to make the mobile terminal (MT) handoff to a suitable network as its will has become
an important issue for the next-generation mobile communication system. In this thesis, we propose a predictive network selection scheme (PNSS) which
employs a utility function and allows the bandwidth, received signal strength, connection cost and delay to be input
parameters of the utility function with which an MT calculates the utility value of a network to determine the target network for handoff.
Note that not only the current parameters of a network but also the parameters in the next time period are considered
in PNSS by using the Markov Chain and state-transition probability on bandwidth to calculate the expected value of bandwidth in the next time period
and using the linear regression and exponential moving average to predict the received signal strength in the next time period.
Therefore, the utility fuction considers both the current and furture network states.
Of course, an MT can select a network with a better current network state expected to be better in the furture as well to reduce the possible handoff.
Additionally, PNSS defines a network pre-selection strategy to classify MTs according to the pre-defined thresholds of velocity and bandwidth request.
For different types of MTs, different preferences are set accordingly.
Finally, simulation results show that PNSS can reduce the blocking probability, average number of handoff,
average connection cost and average delay as compared to the scheme closest to PNSS in the literature.

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