# 臺灣博碩士論文加值系統

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 效用函數被廣泛用於模擬用戶所收到的服務質量。在彈性通訊此類型的傳輸資料中，一般是凹效用函數。最近提出的Elastic Allocation的演算法，在假定資源是可無窮地分割並且每位使用者的下載佇列總是有足夠的資料傳輸的情況下，使得總效用函數最大化。但我們發現，EA的演算法並不保證達到最佳解。事實上，EA所求出的解可能是不可行的。在本文中，我們提出一個改進的Elastic Allocation演算法，能在相同的假設環境下找到最佳解。接著，我們再將此結果及解法，擴展到一個下載佇列是在資料量為正常累積的環境。在實際的系統，分配的資源通常有一個基本單位的限制。因此，我們進一步地針對這種制度，設計在假設為使用者的下載佇列為不斷累積，以及正常累積的演算法。為了減少計算複雜度，我們建議一個近似最佳解的資源分配演算法。最後針對以上演算法，進行模擬來評估所提出的演算法在總效用函數和執行時間上的表現。結果顯示我們提出的算法能達到更好的表現。
 Utility functions are widely used to model user perceived service quality. For elastic traffic, the utility function is often increasing and strictly concave. An elastic allocation (EA) algorithm has recently been proposed to maximize the total utility obtained by users, assuming resource is infinitesimally divisible and queues are constantly backlogged. We found that the EA algorithm is not always optimal. In fact, the solution it obtains can be infeasible. In this paper, we present a modified elastic allocation algorithm which is guaranteed to find the optimal solution under the same assumptions. The result is generalized for a system where queues are generally backlogged. In a real system, there is normally a basic unit for resource. Therefore, we further extend the designs to such a system for both constantly backlogged and generally backlogged queues. To reduce computational complexity, we also propose near-optimal resource allocation algorithms. Simulations are conducted to evaluate the proposed algorithms in terms of utility sum and execution time. Results show that our proposed algorithms perform better than previous work.
 中文摘要 i英文摘要 ii誌謝 iii目錄 iv圖目錄 v表目錄 vi一、Introduction 1二、Problem Formulation 8三、Related Works 8四、Fluid-flow Based Systems 124.1 Constantly backlogged 124.2 Generally backlogged 13五、Resource-block Based Systems 185.1 Constantly backlogged 185.2 Generally backlogged 255.3 Time Complexity Analysis 30六、Resource-block Based Systems 326.1 Investigated system 326.2 Scenario 1 346.3 Scenario 2 37七、Conclusion 42參考文獻 43附錄 45
 [1] D. Angelini and M. Zorzi, “On the throughput and fairness performance of heterogeneous downlink packet traffic in a locally centralized CDMA/TDD system,” in Proc. IEEE VTC-Fall’02, pp.510-514, 2002.[2] W. H. Kuo and W. Liao, ”Utility-based radio resource allocation for QoS traffic in wireless networks,” IEEE Trans. on Wireless Commun., vol. 7, no. 7, pp.2714-2722, Jul. 2008.[3] W. H. Kuo and W. Liao, ”Utility-based resource allocation in wireless networks,” IEEE Trans. on Wireless Commun., vol. 6, no. 10, pp.3600-3606, Oct. 2007.[4] G. Song Y. Li, “Cross layer optimization for OFDM wireless networks – Part I: theoretical frame work,” IEEE Trans. on Wireless Commun., vol. 4, no. 2, pp.614-624, Mar. 2005.[5] G. Song Y. Li, “Cross layer optimization for OFDM wireless networks – Part II: algorithm development,” IEEE Trans. on Wireless Commun., vol. 4, no. 2, pp.625-633, Mar. 2005.[6] Z. Jiang, Y. Ge and Y. Li, “Max-utility wireless resource management for best-effort traffic,” IEEE Trans. on Wireless Commun., vol. 4, no. 1, Jan. 2005.[7] L. Kleinrock, Queueing Systems Vol 2: Computer Applications, New York: Wiley, 1975.[8] A. K. Parekh and R.G. Gallager, “A Generalized Processor Sharing Approach to flow control in Integrated Services Networks - The Single Node Case,” IEEE/ACM Trans. Networking, vol. 1, no.3, pp. 344-357, Jun. 1993.[9] A. Demers, S. Kesha and S. Shenker, “Analysis and simulation of a fair queueing algorithm,” Internetworking Res. Experience, pp. 3-26, Sep. 1990.[10] IEEE Std 802.16-2009, ” IEEE Standard for Local and metropolitan area networks—Part 16: Air Interface for Fixed Broadband Wireless Access Systems,”[11] E. Dahlman, S. Parkvall, J. Sk?匜d, and P. Beming, “3G HSPA and LTE for Mobile Broadband,” New York: Academic, 2007.[12] M. Kaneko, P. Popovski and J. Dahl, ”Proportional fairness in multi-carrier system with multi-slot frames: upper bound and user multiplexing algorithms,” IEEE Trans. on Wireless Commun., vol. 7, no. 1, Jan. 2008.[13] F. P. Kelly, A. K. Maulloo and D. K. H. Tan, ”Rate control in communication networks: shadow prices, proportional fairness and stability,” J. of the Operational Research Society, vol. 49, pp. 237-252, Apr. 1998.[14] H. Kim abd Y. Han, “A proportional fair scheduling for multicarrier transmission systems,” IEEE Commun. Lett., vol. 9, no.3, pp. 210-212, Mar. 2005.[15] X. Zhu, J. Huo, C. Xu and W. Ding,”QoS-guaranteed scheduling and resource allocation algorithm for IEEE 802.16 OFDMA system,” in Proc. IEEE ICC’08, pp. 3463-3468, May 2008.
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 1 在寬頻分碼多工存取系統中的一個新穎的分配碼的方法 2 在多速率及多網路基地台WiFi系統之網路選擇最佳化 3 IEEE 802.16正交分頻多工存取系統 啟發式下行頻譜資源分配機制研究 4 在無線網路中針對效用基礎資源分配之研究 5 具備有限資源及動態資源排序的半強佔式計算服務系統 6 在WiMAX傳載網路下以效能為基礎之群播服務接收者最大化機制之研究 7 在無線網路下影像傳輸的動態資源分配 8 高效率之無線行動管理與資源管理整合機制 9 在無線網路環境下以定價模式為基礎的高效率資源預約方法

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