現代通信技術隨著時間而變化，數據傳輸吞吐量也在迅速增加。如何快速可靠地傳輸數據成為一個重要問題。因此，多輸入多輸出（MIMO）系統成為未來的核心技術之一，因為MIMO系統具有顯著提高傳輸吞吐量和正確性的能力。最大似然（ML）檢測是獲得最優解的方法之一。儘管正確性是不容置疑的，但高度複雜性是最大的缺點。在這項工作中，我們提出了一種基於差分度量和梯度搜索的MIMO系統算法。首先，我們定義差分度量並導出梯度搜索的不同階的差分度量的遞歸計算。然後我們使用ZF和MMSE算法來找到初始序列。通過仿真，我們觀察和研究了不同初始序列在MIMO系統梯度搜索中的作用。

Modern communication technologies are changing with time, and data transmission throughput is also increasing quickly. How to transfer data quickly and reliably becomes an important issue. Therefore, multiple-input multiple-output (MIMO) systems become one of the core technologies of the future, since the MIMO system has the ability to significantly increase transmission throughput and correctness. Maximum Likelihood (ML) detection is one of the means to get the optimal solution. Although the correctness is unquestionable, the high degree of complexity is the biggest drawback. In this work, we present an algorithm for MIMO systems based on differential metrics and gradient search. First, we define the differential measure and derive the recursive calculation of the differential measure of the different orders of the gradient search. Then we use the ZF and MMSE algorithms to find the initial sequence. Through the simulation, we observe and study the effect of different initial sequences in the gradient search for the MIMO system.

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