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研究生：	施博翊 Shih, Po-Yi
論文名稱：	利用指示函數結合梯度搜尋應用於多輸入多輸出系統之偵測方法 Detection of the MIMO System Based on Gradient Search Algorithm with Indicative Functions
指導教授：	張名先 Chang, Ming-Xian
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電腦與通信工程研究所 Institute of Computer & Communication Engineering
論文出版年：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	51
中文關鍵詞：	指示函數、多重輸出多重輸入、最大概似解碼
外文關鍵詞：	Indicative functions, MIMO, Maximum likelihood detection
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現代通信技術日新月異，數據傳輸吞吐量也在快速增長，然而如何快速便捷地傳輸數據成為一個重要的問題。因此，多輸入多輸出（MIMO）系統成為未來的核心技術之一。由於 MIMO 系統變得越來越重要，因此 ML (Maximum likelihood detection) 偵測法是獲得最佳解決方案的方法之一。儘管正確性非常不錯，但高度複雜性是最大的缺點。相反地，ZF 和 MMSE 有著很低的複雜度，但是前者比較起來，錯誤率又增加不少，由此可知追求複雜度和錯誤率之間的平衡是通訊方面研究者的考量重點。在本論文中，我們主要探討指示函數和梯度搜尋理論如何應用在多重輸入和輸出系統的偵測。首先，我們定義指示函數和梯度搜尋理論，並且讓兩者互相搭配，然後經過一些修改後，藉由先確定接受向量中何者是 ML 之解，以此降低搜尋時需要搜尋的點的數量外，能更進一步降低計算的複雜度而且又能維持搜尋中各階層的效能。從而得出這套理論能在效能和複雜度之間達成平衡，同時，把這套理論應用在從不同偵測理論中得到的初始陣列上，並說明和比較這套理論跟一般梯度搜尋所產生結果的不同。

Modern communication technologies are changing with time, and data transmission throughput is also increasing quickly. How to transfer data quickly and eliably becomes an important issue. Therefore, multiple-input multipleoutput(MIMO) Systems[1] become one of the core technologies of the future.For the MIMO systems, Maximum Likelihood (ML) detection can obtain the optimal solution.However, the high degree of complexity is the biggest drawback of ML detection.Zero forcing(ZF) and minimum mean square error (MMSE) methods have lower complexity[2],compared with the ML detection. Although their 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 function and the gradient search algorithm are applied to MIMO system detection. First of all, we study the indicative functions and gradient search algorithm[2][3]. After some modifications, we are able to confirm some ML bits to reduce the number of points that are needed to be searched, and the complexity can be reduced with maintained rate of bit error. Thus, the algorithm can achieve a balance between performance and complexity. We also apply it to another initial sequence which is obtained by another detection algorithm, and explain that the different from tradition gradient search and this algorithm.

摘要 i
Abstract ii
誌謝 iii
Table of Contents iv
List of Tables vi
List of Figures vii
Chapter 1. Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Organization of the Thesis . . . . . . . . . . . . . . . . . . . 2
Chapter 2. Preliminaries 3
2.1 MIMO System Model . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Zero Forcing Detection . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Minimum Mean­Square Error Detection . . . . . . . . . 5
2.4 Maximum Likelihood Detection . . . . . . . . . . . . . . . 6
2.5 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Chapter 3. Introduction of Gradient Search Algorithm 8
3.1 Differential Metrics . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Gradient Search Algorithm of MIMO system . . . . . 12
3.3 Updating the ∆ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 Initial Sequence of the Gradient Search . . . . . . . . . 18
. A. Zero forcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
. B. MMSE algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 20
. C. y
TH detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5 Conclusion of this chapter . . . . . . . . . . . . . . . . . . . 24
Chapter 4. Indicative Functions 25
4.1 Introduction of Indicative Functions . . . . . . . . . . . . 25
4.2 Upperbound and lowerbound . . . . . . . . . . . . . . . . . 27
4.3 Indicative Function in Gradient Search . . . . . . . . . . 29
4.4 Conclusion of this chapter . . . . . . . . . . . . . . . . . . . 40
Chapter 5. New search method in gradient search 41
5.1 Introduction to this chapter . . . . . . . . . . . . . . . . . . . 41
iv
5.2 number of searching times . . . . . . . . . . . . . . . . . . . 41
5.3 New search method in gradient search . . . . . . . . . . 44
Chapter 6. Conclusion 49
References 51
                                    

[1] H. Bolcskei, “Mimoofdm wireless systems: basics, perspectives, and challenges,” IEEE Wireless Communications, vol. 13, no. 4, pp. 31–37, 2006.
[2] M. Chang and W. Chang, “Maximumlikelihood detection for mimo systems based on differential metrics,” IEEE Transactions on Signal Processing, vol. 65, no. 14, pp. 3718–3732, 2017.
[3] M. Chang and W. Chang, “Efficient maximumlikelihood detection for the mimo system based on differential metrics,” in 2015 IEEE Wireless Communications and Networking Conference (WCNC), pp. 603–608, 2015.
[4] D. Gesbert, M. Shafi, Dashan Shiu, P. J. Smith, and A. Naguib, “From theory to practice: an overview of mimo spacetime coded wireless systems,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 3, pp. 281–302,2003.
[5] S. Shafivulla, A. Patel, and M. Z. A. Khan, “Low complexity signal detection in mimo systems,” in 2018 IEEE 88th Vehicular Technology Conference (VTCFall), pp. 1–5,2018.
[6] C. Hung and W. Chung, “An improved mmsebased mimo detection using lowcomplexity constellation search,” in 2010 IEEE Globecom Workshops, pp. 746–750,2010.
[7] 施惟棠, “基於差分度量應用於多輸入多輸出系統之平行搜尋偵測器,” Master’s thesis, 國立成功大學, Jan 2017.
[8] D. Honggui, L. Xiaoxiong, and L. Gang, “The improved maximumlikelihood detection algorithm for mimo systems based on differential metrics,” in 2019 IEEE 11th International Conference on Communication Software and Networks (ICCSN), pp. 353 357,2019.
[9] 時世帆, “利用指示函數協助的多輸入多輸出系統之偵測方法,” Master’s thesis, 國立成功大學, Jan 2018.
[10] M. Chang and S. Su, “Efficient maximumlikelihood detection for the mimo system in hybrid mode,” in 2018 IEEE 88th Vehicular Technology Conference (VTCFall), pp. 1–6, 2018.

校內：2023-08-11公開
校外：不公開電子論文尚未授權公開，紙本請查館藏目錄

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