臺灣博碩士論文加值系統

調變分類是對一個未知的接收訊號，從一個有限集合的調變方式裡去挑出這個未知訊號最有可能是利用哪一種調變方式。雖然調變分類已被探討好幾年了，但對擁有多根天線的系統進行調變分類仍然很少被注意到。除此之外，最近很多關於調變分類的論文也被提出來運用在感知無線電系統上。在知道主要使用者(有執照的使用者)的調變方式之後，感知無線電使用者就可以進一步決定要和主要使用者溝通或是避免干擾主要使用者。我們提出一個針對傳輸端有兩根天線的系統在雷利衰減通道環境下的調變分類器。這個分類器有能力去判斷某個通道是否有人在使用，如果有人在使用的話，它可以更進一步地判斷使用者是使用Alamouti碼或是垂直貝爾實驗室分層空時，並且判斷是使用哪一種的調變方式。我們利用平均概似比率測試來克服未知通道的問題，以及最大概似法則來決定感知無線電使用者所遇到的訊號是屬於哪一種情形。模擬結果顯示此分類器即使在低的訊雜比下仍有極高的正確率。並且我們還把此分類器推廣到接收端(分類器本身)擁有多根天線的情形。為了降低執行複雜度，我們額外提出兩個擁有比較低計算複雜度的演算法。

Modulation classification is to blindly identify the modulation type of the received signals within a set of known constellations. Although modulation classification has been researched for many years, less attention has been paid to classifying the modulation type for systems with multiple transmit antennas. In addition, recently many papers about modulation classification have been published for Cognitive Radio (CR) systems. With the knowledge of
the modulation type that the primary (licensed) user uses, the CR user can either communicate with or avoid interfering primary users. We propose a classifier for a system with two transmit antennas in fading channels. This classifier has the ability to determine whether someone is using the channel or not. If the channel is used, the classifier can determine the modulation type and whether the
user is using Alamouti Code or Vertical Bell Laboratories Layered Space-Time (V-BLAST). We use Average Likelihood Ratio Test (ALRT) approach to overcome the problem of unknown channel gains and Maximum Likelihood (ML) principle to determine the case that the CR user encounters. Simulation results show that the proposed classifier has high accuracy even at low SNR. We also extend this algorithm to the case of multiple receive antennas. To reduce the implementation complexity, two additional algorithms are proposed.

1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Previous Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Notations and Useful Integral Identities . . . . . . . . . . . . . . . . . 5
2 System Models 6
3 Average Likelihood Ratio Test (ALRT) Approach and MIMO Mod
ulation classification 9
3.1 Alamouti Code is Transmitted . . . . . . . . . . . . . . . . . . . . . . 9
3.1.1 ALRT Approach for Alamouti Code . . . . . . . . . . . . . . . 12
3.2 V-BLAST is Transmitted . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.1 ALRT Appronach for V-BLAST . . . . . . . . . . . . . . . . . 16
3.3 The Channel is Idle . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Multi-Antenna Classifier 30
4.1 The algorithm for Multi-Antenna Classifier . . . . . . . . . . . . . . . 30
4.2 Implementation Complexity and Simulations . . . . . . . . . . . . . . 31
5 Modification of the Proposed Algorithm 36
5.1 Modulation Classification Only (M.C.O.) . . . . . . . . . . . . . . . . 36
5.1.1 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.2 Energy Detection Method . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2.1 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
6 Implementation Complexity and Performance Comparisons of Our Proposed Algorithms 54
6.1 Implementation Complexity . . . . . . . . . . . . . . . . . . . . . . . 54
6.2 Performance Comparison . . . . . . . . . . . . . . . . . . . . . . . . . 56
6.3 A More Realistic Case . . . . . . . . . . . . . . . . . . . . . . . . . . 58
7 Conclusions 62
Bibliography 63
Appendix 67

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