在這篇論文裡，我們考慮了多使用者的多輸入多輸出正交分頻多工
(Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing,
MIMO-OFDM) 的系統，並模擬由 IEEE 802.15.3c 工作團隊所制定的六十秭赫
頻帶的通道模型。而多使用者在傳輸過程中會彼此之間造成干擾，而干擾對齊技
術 (Interference Alignment, IA) 是一種可以降低干擾影響的技術。
為了降低干擾對齊演算法的複雜度，我們提出了兩種具有低複雜度的干擾對
齊演算法，混合式干擾對齊 (Mixed Interference Alignment, MIA) 演算法和加權
混合式干擾對齊 (Weighted Mixed Interference Alignment, WMIA) 演算法。混合
式干擾對齊演算法是透過傳送端和接收端由不同的準則所設計，而加權混合式干
擾對齊演算法則是多考慮了使用者之間的權重。我們與既有的演算法做比較，包
含疊代干擾對齊 (Iterative Interference Alignment, IIA) 演算法、最大訊號干擾雜
訊比 (Maximum Signal-to-Interference-Plus-Noise Ratio, MSINR) 演算法和最小
均方誤差 (Minimum Mean Squared Error, MMSE) 演算法，透過模擬分析，可以
得到我們所提出的演算法會具有比較低的複雜度和相當的效能。
而在我們所討論疊代方法的干擾對齊技術下，初始值會影響效能，比起多數
干擾對齊演算法使用隨機的初始值，我們提出了一個具有低運算複雜度的初始值
方法，稱作 (Relaxed Maximum Desired Signal Power, RMDSP)，並且它有更好的
效能。

Interference alignment(IA) was proposed recently for suppressing interference to achieving the optimal degree of freedom of an interference channel. For reducing the complexity of IA algorithms, we present two modied IA algorithms in this thesis. The first IA algorithm is called the mixed IA(MIA) algorithm, which used different criteria to minimize the leakage interference and mean squared error, for designing the precoding matrices and combining
matrices, respectively. The MIA algorithm provides lower complexity than the maximum
signal-to-interference-plus-noiseratio(MSINR) algorithm or the iterative interference alignment(IIA) algorithm, while demonstrating comparable performance. This algorithm results in orthonormal precoding matrices, which possess the advantage of efficient quantization. On the basis of the MIA algorithm, we also propose a weighted mixed IA(WMIA) algorithm, which accounts for the weak user's performance. The WMIA algorithm manages
interference by different weights to distribute power fairly and it also possesses the advantages of low complexity and orthonormal precoders, similar to the MIA algorithm. However,
it demonstrates trade-off performance compared with that of the MIA algorithm in different situations.
The other issue investigated in the thesis is initialization. As IA algorithms do not
guarantee to achieve the global optimum; hence the initialization is crucial for the performance. We propose an initialization method with low complexity that involves maximizing
the desired signal power through the gradient descent method.
In this study, we consider the multi-user multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) interference system. The channel model employed is the 60GHz indoor channel model established by the IEEE802.15.3c task group,
which includes line-of-sight(LOS) and non-line-of-sight(NLOS) situations.

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