從過去到現在，近代通訊系統不斷地演進的過程中，各種類型的系統，像是電腦、手機以及影像系統被要求信號必須大量且可靠地傳送，傳統單一輸入單一輸出系統因此顯得較無效率，許多通信系統設計者因此採用多重輸入輸出的方法來設計系統。
由於傳輸速率以及效率勝過單一輸入單一輸出系統，多重輸入多重輸出系統因此成為無線通訊發展的主流趨勢，因為它能更有效增加頻譜使用效率並大幅提升通訊吞吐量，但由於接收天線上收到全部來自傳輸天線的信號而非單一傳輸天線的信號，也因此增加接收端偵測器的複雜度。大家熟知的ML以及後來球體解碼演算法的提出雖然能有效得到較低的錯誤率，但是偏高的複雜度使得它們必須持續改善，以用於真實系統上面；相反地，ZF以及MMSE方法雖然有很低的複雜度，但是比起前者，錯誤率又增加了不少，由此可見並不存在十全十美的系統，追求複雜度以及錯誤率的平衡是通訊方面的研究者考量的重點。
在本論文中，我們主要探討指示函數如何應用於多重輸入多重輸出系統的偵測。首先，我們定義指示函數的作用以及來源，並推導出不同形式的指示函數，接著互相搭配，然後藉由ㄧ些修改或改善，希望除了能在進行搜尋之前預先確認接收向量中何者是ML的解，以此減低搜尋時需要經過的點的數量以外，能夠更進一步引出指示函數的效能，進而減少更多計算複雜度，從而得出結論確認此套演算法能在效能與複雜度之間取得平衡，最後的模擬過程階段主要是應用前述方法設計出改良的MIMO偵測方法，然後在多天線系統進行模擬與比較，並說明了此套演算法主要受初始序列的影響。

From the past to the present, in the course of the continuous evolution of modern communication systems, various types of systems, such as computers, mobile phones, and imaging systems, have been required to transmit signals in large quantities and reliably. Traditional single-input single-output systems therefore appear to be relatively inefficient. Many communication system designers therefore use multiple input and output methods to design the system.
Since the transmission rate and efficiency outperform the single-input single-output system, the multiple-input multiple-output(MIMO) system has become the mainstream trend in the development of wireless communications because it can effectively increase the spectrum efficiency and significantly increase the communication throughput,The transmitted signals are received on all the receiving antennas, so it also increases the complexity of the receiver detector. Although the well-known ML and the sphere decoding algorithm can effectively obtain lower error rates, the high complexity makes them must continue to be improved for use in real systems. Conversely, although there are ZF and MMSE methods with lower complexity, compared with the former, the error rate has increased a lot. The pursuit of the balance of complexity and error rate are the focus of study.
In this thesis, we study how the indicative functions are applied to MIMO system detection. First of all, we define study the indicative functions, and its different forms. After some modifications and improvements, we are able to confirm more ML bits to reduce the number of points that are needed to be a searched, so the performance of the indicative functions can be further elicited, and reduces the computational complexity. Thus, the algorithm can achieve a balance between performance and complexity. We also explain that this algorithm is mainly affected by the initial sequence.

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