干擾對齊是一項有潛力應用於未來無線通訊網路之基地台間干擾消除的技術，不過標準的干擾對齊方法需要使用完美的通道資訊，因此在非完美通道資訊的情況下，其效能表現會明顯衰退。在本論文中，我們考量通道誤差並針對包含多對傳送/接收裝置之多輸入多輸出干擾通道提出一個具有強健性的迭代式干擾對齊演算法，其中每一次迭代過程的預編碼器與解碼器設計皆以最大化訊號干擾比 (signal-to-interference ratio ; SIR) 的下限 (lower bound) 為準則；預編碼器設計之SIR定義為「對應接收端所欲接收之訊號能量與傳送訊號洩漏至所有非對應接收端之總干擾訊號能量的比值」，而解碼器設計之SIR定義為「所欲接收之訊號能量 (來自對應傳送端) 與洩漏自所有非對應傳送端之總干擾訊號能量的比值」。電腦模擬結果顯示，我們所提出之迭代式干擾對齊方法比基於最小均方誤差準則之現有相關作法具有相當的系統效能與較低的運算複雜度。

Interference alignment (IA) for multiuser multiple-input multiple-output (MIMO) interference channels is a promising technique for interference elimination in future wireless cellular networks. Since the standard IA methods require perfect channel state information (CSI), significant performance degradation would occur under imperfect CSI. In this thesis, we propose a robust iterative IA scheme for MIMO interference channels with multiple transmitter-receiver pairs under an assumption that the channel error is independent and identically complex Gaussian distributed with bounded expected norm. At each iteration, the precoder and decoder designs are updated alternately to maximize a lower bound of the signal-to-interference ratio (SIR); the SIR for the precoder design is defined as the ratio of the desired signal power at the intended receiver to the total interference power leaking from the same transmitted signal to all unintended receivers, while that for the decoder design is defined as the ratio of the desired received signal power from the intended transmitter to the total interference power leaking from all unintended transmitters. The proposed iterative IA approach achieves comparable performance with less computational complexity in comparison with a previous related method based on the minimum mean-squared error criterion for data detection.

Abstract i
Contents ii
List of Figures iii
I. Introduction 1
II. Background 5
A. System Model 5
B. IA Concept 6
C. Effect of Imperfect CSI on IA 7
III. Propsoed Methods 9
A. Initial Precoder Design 9
B. Decoder Design Based on SIR-LB Maximization 9
C. Precoder Design Based on SIR-LB Maximization 12
D. Iterative Design of the Precoder and Decoder 14
E. Convergence and Complexity Analysis 16
IV. Simulation Results 18
V. Conclusion 29
References 30

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