在近年來，隨著科技的進步，多天線輸出輸入(MIMO)系統已經受到許多研究者的關注，使用這樣系統可以使得傳輸率(transmission rate)和吞吐量(throughput)的增加，目前已有許多不同的解調方法已經被提出，有線性解調還有非線性解調，而在對於系統上，複雜度和錯誤率之間的取捨成為一個重要的問題。
在這篇論文中，我們會先介紹球狀解碼的樹狀搜索，並介紹提出的動態軟值演算法，以深度優先的方式來搜尋，在搜尋過程中以給定的分支列表(branch)來動態地決定要搜索的範圍，接著，我們另外加入雜訊統計資訊來取代給定的分支列表作為在不同雜訊比(SNR)下決定的分支列表。在模擬結果中表示，動態搜索的方式可達到低複雜度，在不同SNR下，和最大概似解(ML)的錯誤率(BER)誤差約小於1%。

In recent years, the Multi-Input Multi-Output(MIMO) has attracted much attention in the field of communications. The use of MIMO systems allows us to increase the transmission rate and system throughput. Many kinds of detectors including the linear detectors and nonlinear detectors, for the MIMO system have been proposed. An important issue of study for the MIMO system is on the trade-off between the complexity and bit error rate(BER).
In this thesis, we first review the sphere decoding(SD) algorithm based on the tree search. Then we introduce the proposed dynamic soft algorithm with the depth-first search. During the tree search, the searching range is dynamically determined based on a given branch list. We further adopt the statistics of noise and create the dynamic branch list to replace the given branch list, such that the branch list is determined based on signal-to-noise ratio (SNR) before the searching.
The simulation results show that the performance of the proposed dynamic search algorithm can achieve the performance, approximately 1% error, to the optimal detection while the complexity is reduced.

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