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研究生: 林威宇
Lin, Wei-Yu
論文名稱: 多點跳躍無線感知網路功率效益最佳化
Power Efficiency Optimization for a Multi-hop Cognitive Network
指導教授: 郭文光
Kuo, Wen-Kuang
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電腦與通信工程研究所
Institute of Computer & Communication Engineering
論文出版年: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 42
中文關鍵詞: 多重跳躍無線網路跨層最佳化干擾模型混合整數線性規劃感知無線電
外文關鍵詞: multi-hop wireless network, cross-layer optimization, interference modeling, MILP, cognitive radio
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  • 這篇碩論探討無線感知多重跳躍網路的能量使用最佳化問題。這
    涵蓋了能量控制、頻道共用、流量分配和路由,所以這個最佳化問題
    觸及OSI 模型的實體層、資料連結層和網路層。我們的原始模型是個
    NP-hard 混合整數非線性分數規劃。我們需要對原始模型做線性化才有
    辦法求解。
    我們用沈農公式來計算通道容量,此公式為非凸。我們用切線和割
    線線性化此公式。另一個造成限制式非線性的原因是變數相乘項。這
    部分我們引用了逐段地重新線性化技術來解決。以上兩步讓所有限制
    式為線性。我們用Charnes-Cooper Transform(CCT) 使目標函式不再是
    分數型態。再把部分變數做還原,最後得到一個混合整數線性規劃的
    模型。

    This thesis study optimal power efficiency problem of the multi-hop CR network. The topic contains power control, bands sharing and routing. Thus, OSI model such as physical layer, data link layer, and network layer are included
    in our optimal programming. The original model is a mixed-integer nonlinear fractional , it is NP-hard and hardly solvable. We should process some transforms on it than we can solve it.
    We compute channel capacity by Shannon’s information theorem, and the constraint is nonconvex. We use tangent lines and a secant line to make it convex. Bilinear term is another cause of nonliearity of the constraint. For this part, we apply piecewise reformulation-linearization technique. These relaxation make all the constrains to be linear. And we use Charnes-Cooper transform to reduce the fractional form of objective function, then doing adjustment
    over some variable. After series of transform we get a mixed-integer linear programming problem.

    口試委員會審定書 i 中文摘要ii Abstract iii 致謝 iv Contents v List of Figures vii List of Tables viii 1 Introduction 1 2 System Model 4 2.1 Objective and Constraints 5 2.1.1 Objective function 5 2.1.2 Power Consumption 6 2.1.3 Interference 7 2.1.4 Loading Capacity 7 2.1.5 Flow Conservation 7 2.2 Original Model 8 3 Scheme to Solve 10 3.1 Linear Relaxation 10 3.1.1 Piecewise Reformulation-Linearization Technique 10 3.1.2 Log Term Relaxation 14 3.1.3 Model in MILFP 15 3.2 Fractional Relaxation 15 3.2.1 Charnes-Cooper Transformation 16 3.2.2 Inverse of Binary Relaxation 17 3.2.3 Model in MILP 18 3.3 Branch and Cut 20 4 Numerical Result 22 5 Conclusion 30 Bibliography 32 A Piecewise relaxation of a bilinear term 34 B Depicts of piecewise relaxation 38

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    [2] Y.T. Hou, Yi Shi, and H.D. Sherali. Spectrum sharing for multi-hop networking with cognitive radios. IEEE J. Selected Area in Comm., 26(1):146–155, Jan. 2008.
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    [10] Yi Shi, Y.T. Hou, S. Kompella, and H.D. Sherali. Maximizing capacity in multihop cognitive radio networks under the sinr model. IEEE Transactions on Mobile Computing, 10(7):954–967, Jul. 2011.
    [11] IBM ILOG CPLEX Optimization Studio V12.6. ibm.com/software/products/en/ibmilogcpleoptistud/.
    [12] C.E. Gounaris, R. Misener, and C.A. Floudas. Computational comparison of piecewise-linear relaxations for pooling problems. Ind. Eng. Chem. Res., 48(12):
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    [14] Pedro M. Castro and Joao P. Teles. Comparison of global optimization algorithms for the design of water-using networks. Computers Chemical Engineering, 52(10):
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