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研究生: 黃國展
Huang, Kuo-Chan
論文名稱: 無線通訊系統之多目標功率追蹤控制利用柏拉圖最佳化方法
Multi-objective H2/H∞ Power Tracking Control in Communication System : Pareto Optimal Approach
指導教授: 陳博現
Chen, Bor-Sen
口試委員: 楊昌益
Yang, Chang-Yi
何天讚
Ho, Tan-Jan
張適宇
Chang, Shih-Yu
陳博現
Chen, Bor-Sen
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 通訊工程研究所
Communications Engineering
論文出版年: 2012
畢業學年度: 100
語文別: 英文
論文頁數: 49
中文關鍵詞: 多目標最佳化線性矩陣不等式功率控制直序展頻分碼多重存取柏拉圖最佳解
外文關鍵詞: Multi-objective optimization problem (MOP), linear matrix inequality (LMI), power control, DS-CDMA, Pareto optimal solution
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  • 為了追蹤所需的訊號對干擾及雜訊比例,以實現較高的系統容量和更好的移動通訊系統的通信鏈路質量和回饋功率控制器設計最佳化的訊號干擾和雜訊比追蹤控制。最佳化訊號對干擾及雜訊比例的追蹤問題可視為單一目標的 功率控制問題。然而,為了克服通訊全域延遲、通道衰退與各種雜訊干擾,單一目標的 功率控制的使用,有效地衰減通道延遲的影響,這些干擾,實現強健性的訊號干擾與雜訊比的追蹤。因此,這兩個目標都需要一起被考慮在功率控制設計。所以,本篇論文我們提出多目標 H2/H-infinity功率控制用於直序展頻分碼多重存取系統。然而,本文所考慮的多目標H2/H-infinity功率追蹤控制是一個不容易直接求解的困難設計問題。因此,我們提出了次優解的觀念藉由讓這兩個目標的上界值達到越小來解決多目標H2/H-infinity功率追蹤問題。這個多目標H2/H-infinity功率追蹤問題就能轉換成滿足三個線性矩陣不等式的限制條件來使得所對應的上界值越小,我們稱這問題為線性矩陣不等式限制條件之多目標問題。藉由結合MATLAB裡的線性矩陣不等式toolbox和演化搜尋演算法,我們可以簡單地獲得H2/H-infinity解集合我們稱之為柏拉圖最佳化解可供設計者來選擇。最後,一系列的數值模擬並說明了設計流程來證明這個設計問題在所提出的多目標H2/H-infinity功率追蹤控制問題在直序展頻分碼多重存取系統上的表現。


    In order to track a desired signal-to-interference-plus-noise-ratio (SINR) to achieve high system capacity and better communication link quality for cellular communication systems, a feedback power controller is designed for the optimal SINR tracking control. The optimal SINR tracking problem can be regarded as the single-objective (SO) power control problem. However, in order to overcome round-trip delay, channel fading and noises, the SO power control is used to efficiently attenuate the effect of channel delay and these interferences to achieve robust SINR tracking. Hence, these two objectives are needed in power control design. Therefore, we propose the multi-objective (MO)H2/H-infinity power control for DS-CDMA cellular system in this paper. Therefore, the considered multi-objective H2/H-infinity power tracking control is not easy to solve directly. Hence, we propose to minimize the upper bounds of both objectives to solve the multi-objective H2/H-infinity power control problem from the suboptimal viewpoint. Then the MO H2/H-infinity power control problem is transformed to minimizing two upper bounds under the constraint of three linear matrix inequalities (LMIs), i.e. a LMIs-constrained MO problem (MOP). By combining the LMI toolbox in MATLAB with evolutionary searching algorithm, we could easily obtain a set of H2/H-infinity solutions called Pareto optimal solutions for designer selection. Finally, a numerical simulation is given to illustrate the design procedure and to confirm the performance of the proposed MO H2/H-infinity power control for DS-CDMA cellular system.
    Keywords: Multi-objective optimization problem (MOP), linear matrix inequality (LMI), power control, DS-CDMA, Pareto optimal solution.

    摘 要................................................... i Abstract................................................iii 誌 謝...................................................v Content.................................................vi List of Figures.........................................vii List of tables..........................................viii 1. Introduction ........................................1 2. Power Tracking Control Model in DS-CDMA Cellular Systems ................................................8 2.1 Model of Channel, Base Station and Mobile Station in the Closed-loop Power Tracking Control System...........8 2.2 State-Space Model...................................12 3. Problem Formulation..................................16 4. Multi-objective Optimization Problem.................23 4.1 Concepts of Pareto Optimal Solution [18]............23 4.2 Design procedure....................................26 5. Simulation Results and Discussions...................29 5.1. Simulation Settings................................29 5.2 Effect of weighting factors R1 and R2...............30 5.3 Performance of the MO H2/H-infinity Power Control in a DS-CDMA communication system............................32 5.4 Effect on the Outage Probability....................37 6. Conclusion...........................................39 Appendix................................................41 REFERENCES..............................................47

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