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研究生:溫朝凱
研究生(外文):Chao-Kai Wen
論文名稱:設計及分析高容量多重傳輸多重接收之無線通訊系統
論文名稱(外文):DESIGN AND ANALYSIS OF HIGH-CAPACITY MULTIPLE-INPUT-MULTIPLE-OUTPUT WIRELESS SYSTEMS
指導教授:陳俊才陳俊才引用關係
指導教授(外文):Jiunn-Tsair Chen
學位類別:博士
校院名稱:國立清華大學
系所名稱:通訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:174
中文關鍵詞:通道容量多重輸入多重輸出複製方法
外文關鍵詞:Channel CapacityMultiple-Input-Multiple-OutputReplica Method
相關次數:
  • 被引用被引用:0
  • 點閱點閱:272
  • 評分評分:
  • 下載下載:23
  • 收藏至我的研究室書目清單書目收藏:0
最近的研究顯示,使用多重天線在傳輸端及接收端的技術,可以明顯的提高通道的使用效率,在這領域的挑戰包括如何設計有效率的編碼技術來增加系統的多元性及提高系統的強韌度,以及如何利用高等的訊號處理技術來增加調變及等化的能力,或者是如何設計有效的資源分配演算法,藉以提高系統的頻寬使用效率,另一方面的挑戰是關於如何正確的模擬真實通道以及計算在真實通道環境下系統所能傳輸的最大速率,本論文將涵蓋對於這兩個熱門的研究議題,我們所提出的理論以及解決方式。


本論文可分為五大部分,第一個部分我們設計一高效能的盲目性編碼技術,此一技術將克服在室內無線通訊環境下因頻率選擇效應所帶來的問題,採用此一技術除了不需要在一般通訊系統中常需的訓練數列外,也不需要任何的系統回傳機制,不但如此其計算複雜度也將比原先Raleigh及Cioffi所提出的方法還明顯的降低許多。第二個部分我們指出如果盲目的使用Raleigh及Cioffi所提出的最佳機制,在實際的無線系統環境下未必是最有效率的方式,同時我們提出一有效率的解決方案,稱為數位矩陣多頻角度--頻率編碼技術,此一技術中,
我們利用富立葉編碼的技巧先辨認通道中重要的部分,而後我們僅處理此一重要的部分,我們並分析此一方法所造成的影響及其在降低計算複雜度上所帶來的好處,電腦模擬實驗結果證實我們所提出的技術將僅微小的降低傳輸速率,但卻大幅的減低計算複雜度。
第三個部分我們將此一角度--頻率編碼技術推廣至多用戶環境,有別於髒紙編碼(Dirty Paper Coding)技術所採用的干擾消除技術,我們所設計的多用戶角度--頻率編碼技術將僅利用通道部分資訊來達到避開干擾的優點。


第四個部分我們將推導在有空間相關效應下,系統的相互資訊量及系統所能提供的傳輸速率,我們所考慮的系統是假設在接收端系統擁有通道完整的資訊,而在傳輸端僅擁有部分的通道資訊,利用一致的手法,我們推導在點對點通訊環境下系統可提供傳輸速率的近似解。
第五部分我們考慮在多用戶環境下,系統所能提供傳輸速率的近似解,更進一部的我們利用此一近似解提供一有效率的演算法,此演算法提供當系統僅擁有通道中緩慢變化的資訊時,傳輸訊號相關矩陣的最佳傳輸機制。
Systems that employ multiple antennas in both the transmitter and the receiver of a wireless system have been shown to promise extraordinary spectral efficiency. Research challenges in this area include the development of efficient coding schemes to increase signal diversity and system robustness, advanced signal processing techniques to improve the modulation/equalization efficiency, and better resource assignment algorithms to share limited spectrum among users requiring different data throughputs. On the other hand, researchers in this field have been eagerly trying to characterize the multiple input multiple
output (MIMO) wireless channels in more realistic scenarios hoping to bring the sophisticated MIMO techniques into practical applications in the near future. This thesis present the progress we have made towards designing sophistic space-time signal processing techniques and determining the capacity benefits of multiple antennas under realistic channel scenarios.

The thesis contains five results. First, we develop an efficient architecture with blind adaptive coding schemes in a time division duplexing (TDD) system with slow Rayleigh fading frequency-selective MIMO channels. With this method, neither a training sequence nor feedback of channel information is required in the proposed blind approach. Besides, the computational complexity of the proposed scheme is significantly lower than that of the coding scheme described by Raleigh and Cioffi. Second, we point out that blindly using optimal approaches, such as the discrete matrix multi-tone (DMMT) coding scheme
proposed by Raleigh and Cioffi, is not necessarily efficient in practice. With this perspective, we develop a low complexity space-time coding scheme, DMMT angle-frequency coding scheme (DMMT-AFCS), for wireless communication systems with clustered multipath channels. Making use of a Fourier precoder to identify and process only the dominant clustered spatial channel
structure, we construct the DMMT-AFCS and analyze its performance loss, trade-off of its low complexity. Experimental examples show that, with little sacrifice in system capacity, the computational complexity of the proposed coding scheme is significantly lower than that of the original DMMT coding scheme in realistic wireless channel environments. Third, we extend the notion of the DMMT-AFCS to multiuser wireless scenarios. Instead of employing interference-cancellation-like dirty paper
coding (DPC) strategies, we work toward an interference mitigation coding scheme by exploiting only partial channel
information.

Fourth, we derive the mutual information and the achievable data rate of various MIMO systems with correlated spatial
channels. We consider scenarios where the MIMO channel state information is known at the receiver, but only partially known at the transmitter. In a unified approach, we investigate the asymptotic performance, or equivalently the large-system properties, of various point-to-point systems with antenna-array-based MIMO channels having spatial correlations at both the transmitter and the receiver. Fifth, we use the replica method originally developed in statistical physics to investigate the
asymptotic sum-rate of a Gaussian antenna-array-based MIMO multiple-access wireless channel having spatial correlations at both the transmitters and the receiver. Furthermore, with the asymptotic solution, we provide an efficient iterative water-filling algorithm to determine the optimum transmit signal covariance matrices when only the slow-varying channel spatial covariance information is available.
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Chapter
1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Space-Time Signal Processing Techniques . . . . . . . . . . . . . . . . . . 2
1.2 Fundamental Capacity Limits of Realistic MIMO Wireless Channels . . . 6
2. An Adaptive Spatio-Temporal Coding Scheme for Indoor-Wireless Communication
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1 MIMO Indoor Wireless Channel Model . . . . . . . . . . . . . . . . . . 11
2.2 Discrete Matrix Multitone . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 DMMT-SFCS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Blind Adaptive DMMT-SFCS for a TDD MIMO System . . . . . 17
2.3 Channel Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 Experimental Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3. A Low Complexity Space-Time OFDM System for Clustered Wireless Multipath
Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.1 DMMT-AFCS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Performance Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.1 Magnitude gain and resolution gain . . . . . . . . . . . . . . . . . 44
3.2.2 Performance in the Multi-Ring Channel . . . . . . . . . . . . . . 47
3.3 Experimental Examples and Discussions . . . . . . . . . . . . . . . . . . 50
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4. A Low Complexity Space-Time OFDM Multi-User System . . . . . . . . . . . 56
4.1 MU-AFCS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Theoretical Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.1 Capacity of OFDMA . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.2.2 Capacity of MU-AFCS . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3 Experimental Examples and Discussions . . . . . . . . . . . . . . . . . . 65
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5. Asymptotic Analysis of MIMO Wireless Systems with Spatially-Correlated Channels
: Joint-Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.1 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2 Mutual Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.2.1 Gaussian Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2.2 BPSK/QPSK Inputs . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.3 A Water-Filling Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6. Asymptotic Analysis of MIMO Wireless Systems with Spatially-Correlated Channels
: Separate-Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.1 Conditional Mean Estimator . . . . . . . . . . . . . . . . . . . . . . . . . 87
6.2 Mutual Information of MIMO Systems . . . . . . . . . . . . . . . . . . . 90
6.2.1 Separate-Decoding Systems With Linear Spatial Equalizers . . . . 91
6.2.2 Separate-Decoding Systems with Nonlinear Spatial Equalizers . . 98
6.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7. Asymptotic Spectral Eciency of MIMO Multiple-Access Wireless Systems Exploring
Only Channel Spatial Correlations . . . . . . . . . . . . . . . . . . . . 105
7.1 Channel Model and Problem Formulation . . . . . . . . . . . . . . . . . 108
7.2 Asymptotic Sum-Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.2.1 Free Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.2.2 Practical Considerations . . . . . . . . . . . . . . . . . . . . . . . 116
7.2.3 Physical Meanings . . . . . . . . . . . . . . . . . . . . . . . . . . 118
7.3 Sum-Rate Maximization . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
7.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
8. Summary and Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Appendix
A. The Parallel Subchannel Gains of the DMMT-SFCS and the DMMT-AFCS . . 136
B. An Asymptotic Result of the Free Energy (5.13) . . . . . . . . . . . . . . . . . 139
C. Proof of Lemma 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
D. Proof of Property 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
E. The proof of Proposition 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
F. Proof of Property 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
G. Proof of Lemma 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
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