無線/行動通信技術的進步已經深深地影響我們的生活。在生活中有越來越多的用戶將傳統固定式的裝置換成行動裝置，並作為主要的隨身配備。一旦行動網路的頻寬無法滿足日益增加的需求，網路的壅塞將是無可避免的。正如大家所知，連結允入控制可以藉由管制使用者進入系統的數量，來降低壅塞的情況。然而傳統的連結允入控制並無法讓使用者有效地使用網路資源。其中一個提出的方法是壅塞定價，它藉由改變收費價格來影響使用者的通訊行為。在本篇論文中，我們藉由結合連結允入控制、壅塞定價和雙重門檻來最大化細胞式網路的收益。在我們的模型中我們考慮兩種情境：多細胞和單細胞。在每一個細胞中都有兩種連結抵達:新連結和交遞連結。為了給交遞連結較高的優先權，我們加入保護通道的機制。在單細胞情境中，交遞連結抵達速率和新連結抵達速率彼此獨立。在多細胞情境中，我們假設網路是同質的，而每個細胞是統計上相同的。值得注意的是，交遞連結抵達速率和新連結抵達速率以及其他系統參數是相關的。我們在每個情境中考慮四種連結允入控制機制: (1) 沒有壅塞定價機制的連結允入控制 (2) 壅塞定價機制永遠啟動的連結允入控制 (3) 連結允入控制使用單一門檻的壅塞定價機制 (4) 連結允入控制使用雙重門檻的壅塞定價機制。我們也考慮兩種流量負載情況：靜態流量負載和非靜態流量負載。在靜態流量負載中，新連結抵達速率是固定的，而在非靜態流量負載，新連結抵達速率會隨時間改變。我們針對所考慮每一個情境中的四個連結允入控制機制，開發解析模型。我們利用VISUAL C++來撰寫相關的模擬程式，來驗證解析結果的準確性。我們研究多個系統參數對於效能指標的影響，例如，新連結抵達速率、服務速率、駐留速率。我們感興趣的效能指標包括，新連結阻擋機率、交遞失敗機率、新連結放棄機率、加權重阻塞機率、整體效用、整體收益。

The progress of the wireless/mobile communication technology has profoundly affected our life. There are more users becoming dependent on the mobile devices as their primary computing devices and replacing the traditional stationary hardware. Once the mobile netwrok bandwidth can’t satisfy the growing demand, congestion in the network is inevitable. As is well known, Call Admission Control (CAC) can reduce congestion by controlling the number of users into the network. However, traditional CAC is not enough to make users use the network resource more efficiently. One of the proposed approaches is congestion pricing that can affect user's communication behavior by changing the charged price. In this work, we study how to maximize the revenue by combining call admission control, congestion price and the double-threshold scheme in cellular networks. We consider two scenarios in our model: multi-cell and single-cell. There are two classes of calls arriving at each cell: new and handoff. And to give handoff calls priority over new calls, we use the guard channel mechanism in our system. In single-cell scenarios, the handoff call arrival rate is assumed to be independent of the new call arrival rate. In multi-cell scenarios, we assume that the network considered is homogeneous, i.e., every cell is statistically identical. It is noted that the handoff call arrival rate depends on new call arrival rate and other system parameters. We consider four CAC schemes in each scenario: (1) CAC without congestion price, (2) CAC with congestion price always enforced, (3) CAC with single-threshold congestion pricing and (4) CAC with double-threshold congestion pricing. We also consider two traffic load cases: stationary and non-stationary. For the stationary cases, the new call arrival rate is fixed over time, whereas for the non-stationary cases, the new call arrival rate will vary over time. We develop the analytical models for all four CAC schemes in each scenario considered. The associated simulation programs are written in visual C++ to verify the accuracy of the analytical results. We investigate the effect of various system parameters, e.g., the new call arrival rate, the service rate, the dwell rate, on the performance measures. The performance measures of interest include new call blocking probability, handoff failure probability, give-up probability, weighted blocking probability, aggregate utility, and revenue.

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