Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/6196
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dc.contributor.advisor陳曉華zh_TW
dc.contributor.advisorHsiao-Haw Chenen_US
dc.contributor.author李正雄zh_TW
dc.contributor.authorLee, Cheng Hsiungen_US
dc.date2000zh_TW
dc.date.accessioned2014-06-06T06:37:33Z-
dc.date.available2014-06-06T06:37:33Z-
dc.identifier.urihttp://hdl.handle.net/11455/6196-
dc.description.abstract本篇論文主要提出一種新的多用戶偵測方法,它是傳統最小均方誤差(MMSE) 的改善。一般的MMSE檢測方法需要大量的記憶體和檢測的時間延遲,所以,硬體的複雜度很高不易實現且不能即時偵測。我們提出的改善方法稱之為Blocked MMSE (B-MMMSE) 多用戶檢測,在這個檢測方法中犧牲了頻寬的使用效率,它用一個”silence”位元將傳送的資料位元分割成區段。每個區段彼此之間互不干擾,所以,B-MMSE檢測可以在每個區段時間內作完全獨立的檢測。因為分割的每個區段長度可以視情況作調整,所以,B-MMSE在不影響檢測性能的前提下,硬體實現的複雜度可以有很大的彈性。我們拿B-MMSE和另一種多用戶偵測方法Decorrelator做比較,雖然B-MMSE的位元錯誤率會優於Decorrelator偵測,但是它有個缺點,必須預先估測使用者傳送訊號的功率。 依據適應性濾波器理論 (Adaptive Filter Theory),可發現B-MMSE檢測器可以等效地用適應性濾波器加以實現,稱之為Adaptive B-MMSE 檢測器。我們選擇了Least Mean Square (LMS)和Recursive Least Squares (RLS)兩種演算法作為適應性濾波器的機制,並且加以比較這兩種演算法的優缺點。Adaptive B-MMSE 改善了之前提到的B-MMSE主要缺點,它不需要估測訊號的傳送功率,而且這種適應性的偵測適用於時變的傳輸通道中。另外,它的硬體複雜度跟使用者的個數和分割區段的長度呈線性的關係。 為了有效降低檢測的位元錯誤機率,我們考慮Adaptive B-MMSE檢測器和Adaptive antenna array合併使用時的模擬。使用Adaptive antenna array後,我們可以調整每個天線的權重值,讓整個陣列天線形成的波束主要對準感興趣的使用者,且把波束的零點(null)對準其他的干擾使用者,也就是先對接收訊號作空間的濾波動作。透過陣列天線我們可以保持感興趣的訊號,而有效地抑制了其他使用者的干擾。所以,由模擬結果可發現隨著陣列天線的使用,可以有效地改善Adaptive B-MMSE偵測器的效能。zh_TW
dc.description.abstractIn this thesis, we introduce the Blocked MMSE (B-MMSE) multiuser detection scheme for asynchronous DS/CDMA systems, it can be considered as a modified version of conventional MMSE. In the B-MMSE multiuser detector, the information data stream is divided into blocks by inserted silence bits. Since the block length in the B-MMSE detection is controllable, it provides a great flexibility for practical implementations without destroying the optimality of the MMSE detection. Therefore, the adaptive B-MMSE detector retains the advantages of the MMSE detection. In order to avoid estimation users' power and track the time-variant channel, the B-MMSE is implemented in an adaptive way. The Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms are introduced in adaptive B-MMSE detector. We compare the convergence properties and estimation error between the two adaptive algorithms. It is shown that the RLS algorithm offers a consistently fast convergence regardless of the received power, making it ideal for the use in a time-varying channel with near-far effect. To get the better performance, the antenna array is used in adaptive B-MMSE detector. The LMS and RLS algorithm are also used to control the weights of antenna array. The BER performance of adaptive B-MMSE detector is reduced drastically as the No. of antenna element increased. We also consider using minimum variance distortionless response (MVDR) beamformer but the angle of interest is required. For the practical implementation, we introduce the DOA pre-processor followed by the MVDR beamformer. The BER performance is better than using RLS beamformer if without angle estimation error. Finally, the conclusions and future work are given.en_US
dc.description.tableofcontentsCONTENTS 中文摘要 i Abstract ii Acknowledgements iii Contents iv List of Figures vi List of Tables xii Nomenclature xiii 1 Introduction 1 1.1 Direct Sequence Code Division Multiple Access System 1 1.2 Motivation and Background of Multiuser Detection 3 1.3 Organization of the Thesis 8 2 Analysis of Linear Blocked-Multiuser Detection System 11 2.1 Introduction 11 2.2 System Model 12 2.3 Blocked Decorrelator 17 2.3.1 Blocked Decorrelator in AWGN channel 17 2.3.2 Blocked Decorrelator in multipath channel 18 2.4 Blocked Minimum Mean Squared Error 22 2.4.1 Blocked Minimum Mean Squared Error in AWGN channel 22 2.4.2 Blocked Minimum Mean Squared Error in multipath channel 24 2.5 Numerical Results in Stead State 27 2.6 Conclusion 31 3 Adaptive Implementation of B-MMSE Detector with the LMS and RLS Algorithms 43 3.1 Introduction of Adaptive B-MMSE 43 3.2 Steepest Descent Algorithm 47 3.3 Least Mean Square Algorithm 50 3.4 Recursive Least Squares Algorithm 54 3.5 Simulation Results 58 3.6 Conclusion 63 4 Adaptive Space-Time B-MMSE Multiuser Detector 78 4.1 Antenna array fundamentals 78 4.2 Optimization weight by MVDR Beamformer 81 4.3 Adaptive Space-Time B-MMSE Detector 84 4.4 Simulation Results 90 4.5 Conclusion 93 5 Optimized Adaptive Space-Time B-MMSE Multiuser Detector 104 5.1 Motivation 104 5.2 Improved Adaptive Space-Time B-MMSE Detector 105 5.3 Adaptive Time-Space B-MMSE Detector 110 5.4 Simulation Results 113 5.5 Conclusion 115 6. Conclusions and Future Work 128 6.1 Conclusions 128 6.2 Future Work 130 REFERENCES 131 APPENDIX A The Adaptive Algorithm of DOA Pre-processor 134 APPENDIX B Matlab Programs 138zh_TW
dc.language.isoen_USzh_TW
dc.publisher電機工程學系zh_TW
dc.subjectCDMAen_US
dc.subject分碼多工zh_TW
dc.subjectAdaptive filteren_US
dc.subjectArray Signalen_US
dc.subjectMultipath channelen_US
dc.subjectMMSEen_US
dc.subject自適應性濾波器zh_TW
dc.subject陣列訊號zh_TW
dc.subject多路徑通道zh_TW
dc.subject最小均方誤差zh_TW
dc.title自適應性分碼多工陣列訊號檢測zh_TW
dc.titleAdaptive CDMA Array Signal Detectionen_US
dc.typeThesis and Dissertationzh_TW
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en_US-
item.fulltextno fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeThesis and Dissertation-
Appears in Collections:電機工程學系所
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