在這篇碩士論文中，我們考慮多輸入單輸出(multiple-input single-output, MISO)干擾通道(interference channel, IFC)，並研究能有效對抗通道狀態資訊(channel state information, CSI)錯誤之協調式波束成型演算法。此穩健協調式波束成型設計(robust coordinated beamforming design) 可以被表述成一個最佳化問題: 在每一個傳送端都有功率預算限制情況下，最大化通道狀態資訊誤差落入一球體的範圍內之最差可達到的加權傳輸速率總和。最大化最差加權傳輸速率總和(worst-case weighted sum rate maximization, WC-WSRM)通常是非凸(nonconvex)及NP-hard的問題，並且也被認為是在無線蜂巢式系統中極具挑戰性的重要問題之一。因此，我們專注於設計有效的近似方法來處理此最佳化問題。藉由使用加權最小均方誤差(weighted minimum mean squared error, WMMSE)將問題重新表述以及分塊依次最小化上界(block successive upper bound minimization, BSUM)的方法，我們提出一個有效的穩健型加權傳輸速率最大化演算法(robust weighted sum rate maximization, RWSRM)，並且使用這個演算法處理WC-WSRM的問題可以獲得高性能的近似解。有鑑於目前最先進的演算法都不能得到穩定點(stationary point)的解，而RWSRM演算法已被證明可以收斂到WC-WSRM問題的穩定點，因此可以獲得更好的性能。此外，由於RWSRM演算法可以完全平行化地實現，這使得它更適用於分散式系統。RWSRM演算法也可以被擴展至多輸入多輸出(multiple-input multiple-output, MIMO) IFC的情況。模擬結果證實RWSRM演算法的效能-在同等級的計算複雜度下可達到優於目前最先進的兩種演算法的性能。

Chinese Abstract i
Abstract iii
Acknowledgments v
Contents vi
List of Figures viii
Mathematical Notations ix
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Signal Model and Problem Statement 5
2.1 Signal model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Review of Block Successive Upper Bound Minimization (BSUM) Method 8
3.1 Introduction of BSUM Method . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Convergence of BSUM Method . . . . . . . . . . . . . . . . . . . . . . . . . 9
4 Proposed Robust Beamforming Design for K-user MISO IFC 10
4.1 Problem Reformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
5 Extension: Robust Beamforming Design for K-user MIMO IFC 19
5.1 Signal Model and Problem Statement . . . . . . . . . . . . . . . . . . . . . . 19
5.2 Problem Reformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.3 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6 Simulation Results 30
6.1 Simulation Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.2 Experimental Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
7 Conclusion and Future Directions 36
Appendix A 39
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