未來無線通訊系統主要發展方向為提供具有高傳輸速率與高傳輸品質的多重接取技術。多輸入多輸出技術利用多根天線在傳送端和接收端之間傳輸，來達到高傳輸速率，但是也帶來解調上的問題。為了達到高傳輸品質，最理想的方法為使用最大概似偵測法來得到最佳偵測解，但是產生的複雜度太大，無法實際實行。

QRD-M 演算法和球狀解碼演算法是在多輸入多輸出偵測器中，具有低複雜度和低錯誤率的兩種偵測方法，差別在於前者搜尋方式採用寬度優先；後者採用深度優先。不過球狀解碼在天線個數或星座點多的時候，複雜度會呈指數增加；而QRD-M在訊雜比低的時候，也會有較高的複雜度。這篇論文主要利用混合這兩種演算法來改善以上情形，來達到更低的複雜度，而產生的字元錯誤率也可以接近最佳偵測解。本文使用的QRD-M 演算法是根據通道狀況改變每一層欲保留的分枝個數和欲延伸的分枝個數，所以比先前的QRD-M 演算法具有更低的複雜度。我們提出的方法為先使用QRD-M延伸固定階層，接下來由產生的殘餘路徑中，選擇路徑長最短的路徑開始做球狀解碼。另外，為了根據通道狀況和雜訊功率來選擇QRD-M的層數，來達到更低複雜度，我們還提出適應性混合式演算法。模擬結果顯示，在天線個數多和訊雜比低的時候，我們提出的演算法可以有效的降低球狀解碼和QRD-M的複雜度(尤其是加上適應性混合式演算法)，而且產生的錯誤率也可以接近最大概似偵測法。

The main developing direction for the future wireless communication technologies is to provide high data rate and highly reliable link quality in multiple access systems. Multiple-input multiple-output transmission techniques use multiple antennas at both transmitter and receiver sides to achieve higher capacity, but they usually requires more sophisticated detection algorithms. Although the maximum-likelihood (ML) algorithm provides the best solution, the complexity is too large for many practical systems.

The QRD-M algorithm and sphere decoding are two different MIMO detection algorithms that possess reduced complexity and low error rates. The primary difference between the two is t hat the former uses the breadth-first tree search, while the latter uses the depth-first tree search. The complexity of sphere decoding increases exponentially with the number of antennas and the constellation size, and depends heavily on the reliability of the starting point of the depth-first tree search. To lower the complexity, we propose to improve the reliability of the starting point by performing a few stages of breadth-first tree search before the depth-first tree search is initiated. To keep the complexity overhead of the breadth-first search stages low, we propose to use the QRD-M algorithm. To reduce the complexity further, we adapt the number of breadth-first stages based on the channel conditions and noise powers. The simulation results demonstrate that the proposed adaptive algorithm has a lower complexity than that of the QRD-M and sphere decoding in many signal conditions.

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