臺灣博碩士論文加值系統

近年來，由於無線通訊的迅速發展，漸漸地在日常生活中佔了一席之地。隨著使用者數目的增加以及傳輸品質的要求，對於高速率傳輸的無線通訊系統也日與劇增，因此，也發展出相當多的無線通訊系統，如：超寬頻(UWB)系統、802.11/a/b/g、無線寬頻接取業務(WiMax)、正交分頻多工(OFDM)系統、多輸入多輸出之正交分頻多工(MIMO-OFDM)系統，其中的超寬頻技術由於在室內具有高傳輸的技術而備受矚目，此外，超寬頻系統同時也具有低生產成本、低干擾、低必v消耗以及精確的定位能力。
本篇論文旨在探討直序展頻技術的超寬頻系統之多用戶偵測。由於直序展頻技術的超寬頻系統主要是應用在室內的無線傳輸，因此難免會受到室內障礙物的阻擋而造成多路徑干擾，為了有效率地克服多路徑干擾的問題，我們採用了最大組合耙型接收器。此外，隨著使用者數目的增加，多用戶直序超寬頻系統也引起嚴重的多用戶干擾，所以我們便採用了匹配濾波器去匹配出每位使用者的位元資訊。從理論分析與模擬中，我們得知最大相似度多用戶偵測器可以提供最佳的效能，然而最大相似度多用戶偵測器的運算複雜度會隨著使用者成指數成長。最小均方誤差多用戶偵測器和解相關性多用戶偵測器也提供了次佳的效能，但是它們相關性矩陣的反矩陣並不容易在硬體上實現。
因此我們採用了可近似最大相似度的霍普菲爾類神經網路來降低最大相似度多用戶偵測器的複雜度，然而傳統的霍普菲爾類神經網路容易進入局部最小值。為了改善局部最小值的問題，我們採用了一種名叫模擬退火演算法的最佳化方法應用於霍普菲爾類神經網路中，此最佳化方法可以避免陷入局部而獲得全域的最小值。從實驗結果，在直序展頻技術的超寬頻系統中，使用退火霍普菲爾類神經網路偵測器可以得到不錯的性能，而且在硬體上也容易實現。

Recently, wireless communications are more momentous in daily life as a result of their fast development. With the increasing number of users and the request of transmission quality, wireless systems with high transmission rate increase gradually; therefore, many wireless techniques are developed, such as ultra-wide band (UWB) systems, IEEE 802.11a/b/g, worldwide interoperability for microwave access (WiMax), orthogonal frequency-division multiplexing (OFDM), multiple-input multiple-output OFDM (MIMO-OFDM). Among these techniques, UWB systems attract many attentions due to their high transmission rate for indoor environment, low implementation cost, low interference, low power consumption and precise positioning capability.
The main idea of this thesis is to discuss the multiuser detections of direct sequence-UWB (DS-UWB) systems. The DS-UWB systems are mainly applied in the wireless transmission of indoor environment in which are many obstacles to cause serious multipath interference. In order to overcome the problems of multipath interference efficiently, we adopted the maximal ratio combining (MRC) Rake receiver. Besides, with the increasing number of users, the multiuser DS-UWB systems will cause serious multiuser interference. Consequently, we adopted matched filter to match bit information of each user. According to theoretical analyses and simulations, we know that the multiuser detector with maximum likelihood (ML) estimation can provide the best performance. On the other hand, its computational complexity will grow exponentially with the number of the users. The multiuser detectors that employ decorrelating and minimum mean square error (MMSE) can give the suboptimal performance; however, the structure of hardwire is hard to be implemented as a result of inverse matrix of their crosscorrelation matrix R.
Hence, we adopted the Hopfield neural network (HNN) that can be approximated to maximum likelihood estimation for DS-UWB system. However, the conventional HNN algorithm can converge easily to partial minimum. In order to improve this problem, we proposed modified HNN based on simulated annealing (SA) method which can avoid local minimum and obtain global minimum. From simulation results, the HNN detector based on SA method can provide an attractive performance and be implemented easily on hardware in the multiuser DS-UWB systems.

Contents

Acknowledgments i

Abstract in Chinese ii
Abstract in English iv

Contents…. vi

List of Figures and Tables viii
Figures viii
Tables ix

Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Organization of Thesis 3

Chapter 2 Multiuser Detection for DS-UWB Systems 4
2.1 System Overview 4
2.2 System Model 5
2.2.1 Transmitter Model 5
2.2.2 Multipath Channel Model 6
2.3 Receiver Architecture 6
2.3.1 Conventional Detector 7
2.3.2 Decorrelating Detector 9
2.3.3 Minimum Mean Square Error Detector 10
2.3.4 Maximum Likelihood Detector 11
2.4 Simulation Results and Discussions 12

Chapter 3 Multiuser Detection Using Hopfield Neural Network for DS-UWB Systems 20
3.1 Introduction to Hopfield Neural Network 20
3.2 Hopfield Neural Network Detector 25
3.3 Simulation Results and Discussions 28

Chapter 4 Multiuser Detection Using Simulated Annealing Hopfield Neural Network for DS-UWB Systems 35
4.1 Introduction to Simulated Annealing Algorithm 35
4.2 Simulated Annealing Hopfield Neural Network Detector 38
4.3 Simulation Results and Discussions 41

Chapter 5 Conclusions 45
5.1 Conclusions 45
5.2 Future Work 46

Appendix A 47
Appendix B 49
Appendix C 51

References.. 53

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