運用於上行多點協作通訊下之干擾對齊技術與可彈性規劃之特徵值運算架構設計

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：45

、訪客IP：3.144.109.107

姓名

艾美莉(Amalia Eka Rakhmania) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

運用於上行多點協作通訊下之干擾對齊技術與可彈性規劃之特徵值運算架構設計
(An Interference Alignment (IA) Technique for Uplink Coordinated Multi-Point (CoMP) and Eigen-solver for Rank Deficiency Matrix)

相關論文

★ 具輸出級誤差消除機制之三位階三角積分D類放大器設計	★ 應用於無線感測網路之多模式低複雜度收發機設計
★ 用於數位D類放大器的高效能三角積分調變器設計	★ 交換電容式三角積分D類放大器電路設計
★ 適用於平行處理及排程技術的無衝突定址法演算法之快速傅立葉轉換處理器設計	★ 適用於IEEE 802.11n之4×4多輸入多輸出偵測器設計
★ 應用於無線通訊系統之同質性可組態記憶體式快速傅立葉處理器	★ 3GPP LTE正交分頻多工存取下行傳輸之接收端細胞搜尋與同步的設計與實現
★ 應用於3GPP-LTE下行多天線接收系統高速行駛下之通道追蹤與等化	★ 適用於正交分頻多工系統多輸入多輸出訊號偵測之高吞吐量QR分解設計
★ 應用於室內極高速傳輸無線傳輸系統之設計與評估	★ 適用於3GPP LTE-A之渦輪解碼器硬體設計與實作
★ 下世代數位家庭之千兆級無線通訊系統	★ 協作式通訊於超寬頻通訊系統之設計
★ 適用於3GPP-LTE系統高行車速率基頻接收機之設計	★ 多使用者多輸入輸出前編碼演算法及關鍵組件設計

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

干擾對齊技術可消除無線通訊系統中的干擾源，若結合LTE-A系統中多點協作技術則可以提高蜂胞邊緣使用者的傳輸率與通訊效能。在本論文中，我們提出適用於上行鏈結多點協作系統中之新的干擾對齊技術，在發送端的預編碼器採用最大化每一用戶之信號與洩漏加雜訊能量比(signal-to-leakage-and-noise ratio, SLNR)為設計準則，另一方面，解碼器端則採用以用戶考量為出發點的最大化訊號與干擾加雜訊能量比(signal-to-interference-and-noise ratio, SINR)為設計準則。透過兩者相結合的運算方式使得通道互易性(channel reciprocity)的限制可被去除，因此特別適合操作於上行使用者具有不同發送功率的情形。透過檢視遞迴疊代的過程，以每一用戶為基礎的預編碼與解碼器設計準則可有效抑制同一用戶之天線間干擾，因而比起傳統的干擾對齊演算法，像是最小加權洩漏、以空間串流為基準之最大化訊號與干擾加雜訊能量比或是混和型演算法，具有較佳之性能。
在各式干擾對齊演算法中，特徵值分解(eigenvalue decomposition, EVD)扮演著關鍵的角色。在我們提出的方法中，需要計算信號的特徵值子空間，相反的，最小加權洩漏演算法需要計算信號的特徵值零空間，而所需處理的皆為矩陣秩數不足的情況。因此我們基於QR分解(QRD)提出一一致化且具有彈性之特徵值分解運算架構，首先，將矩陣轉換為Hessenberg型式以降低其後的運算複雜度。其次，根據欲求取零空間或是非零特徵值而利用不同的QR分解方式進行運算。我們所提出的方法架構可適用於不同的矩陣大小，相較於滿秩矩陣，所提出的方法可有效解省運算複雜度，並可得到正確的特徵值運算結果。

摘要(英)

Interference alignment (IA) is a technique to eliminate the interference in wireless communication system. Combined with coordinated multi-point (CoMP), this method could improve the system sum rate performance for cell edge user in LTE-A system. In this thesis, a new IA algorithm for uplink coordinated multi-point (CoMP) is proposed. The unselfish per-user signal-to-leakage-and-noise ratio (SLNR) criterion is used to design the precoder. On the other hand, the design of decoder adopts the selfish algorithm, per-user signal-to-interference-and-noise-ratio (SINR). The combination of both method does not need the channel reciprocity assumption and thus is suitable to operate in the case of different user transmission power. Through iterative procedure, we show that the per-user-based criterion which keeps user data streams orthogonal can suppress interference effectively and achieve higher sum rate than the conventional IA algorithms, such as minimum weighted leakage interference (min leakage), maximum per-stream SINR algorithms, and the hybrid IA in the multi-user CoMP joint reception scenarios.
Eigenvalue decomposition (EVD) plays a key role for our proposed algorithm as well as the conventional algorithm, min leagake method. In our proposed method, EVD is needed to compute the signal subspaces. On the contrary, min leakage method needs the calculation of interference subspace. The decomposed matrix of both IA methods is always rank-deficient. A new eigen-solver based on QR decomposition (QRD) with shift is presented. Hessenberg reduction is implemented in the first stage to reduce the computation complexity. The proposed method could find the correct eigenpairs for both full rank and rank deficient matrix. The architecture of proposed method is shown to be more flexible for any matrix size and has less complexity than the existing method. The proposed eigen-solver could save up to 92% hardware complexity than the conventional EVD to find the nullspacefor hermitian symmetric matrix when the rank of the matrix is quite small . This method is reliable to be implemented due to its equal performance compared to MATLAB “eig” function.

關鍵字(中)

★ 干擾對齊
★ 特徵值分解
★ 多點協調

關鍵字(英)

★ interference alignment
★ eigenvalue decomposition
★ Coordinated Multi-Point (CoMP)

論文目次

Abstract i
Acknowledgement ii
Table of Contents iii
List of Figures vi
List of Tables viii
List of Algorithm ix
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Objective 2
1.3 Thesis Organization 3
Chapter 2 Interference Alignment (IA) in Coordinated Multi-Point (CoMP) 4
2.1 Basic Principle of Interference Alignment 5
2.1.1 Feasibility of Interference Alignment 6
2.1.2 Reciprocity in Interference Alignment 6
2.2 Interference Alignment Method 7
2.2.1 Minimum Weighted Leakage Interference (Min Leakage) 8
2.2.2 Maximum Signal to Interference and Noise Ratio (Max SINR) 9
2.2.3 Hybrid Interference Alignment 11
2.3 Coordinated Multi-Point (CoMP) 12
2.3.1 CoMP Architectures 12
2.3.2 CoMP Schemes 14
2.4 Interference Alignment in CoMP 15
Chapter 3 Proposed Interference Alignment Method in Uplink CoMP 19
3.1 Per-user-based Interference Alignment Algorithm 19
3.1.1 Per-user Max SINR 20
3.1.2 Per-user Max SLNR – Max SINR 22
3.2 Simulation Environment 23
3.3 Convergence Evaluation 24
3.3.1 Interference Leakage and Noise Reduction and Signal Power Amplification 24
3.4 Sum Rate Evaluation 26
3.5 Imperfect Channel State Information Effect 28
3.6 Non Uniform Transmit Power Effect 29
Chapter 4 Eigenvalue Decomposition Method 34
4.1 Basic Principle of Eigenvalue Decomposition 34
4.2 Eigenvalue Decomposition Method 35
4.2.1 Power Method 35
4.2.2 Inverse Power Method 36
4.2.3 Gauss Jordan Method 37
4.2.4 QR Method 38
4.3. QR Decomposition Algorithm 39
4.3.1. Gram-Schmidt Algorithm 39
4.3.2. Householder Transformation 41
4.3.3. Givens Rotation 42
4.3 Coordinate Rotation Digital Computer (CORDIC) 43
4.4 Eigenvalue Decomposition using Systolic Array 45
Chapter 5 Proposed Eigen-solver for Rank Deficiency Matrix 47
5.1 Eigenvalue Decomposition for Rank Deficiency Matrix 47
5.2 Proposed Eigen-solver Algorithm 48
5.2.1 The Computation of Shift 48
5.2.2 Find The Eigenpairs for Nonzero Eigenvalue 49
5.2.3 Find The Eigenpairs for Zero Eigenvalue 51
5.3 Proposed Eigen-solver Architecture 55
5.3.1 Processing Elements (PEs) Block 56
5.3.2 Hermitian Block 59
5.4 Accuracy Evaluation 62
5.5 Complexity Evaluation 63
5.6 Implementation of Proposed Eigen-solver in Interference Alignment 71
Chapter 6 Conclusion 74
Bibliography 76

參考文獻

S. Mamoru, K. Yoshihisa, A. Morimoto, D. Nishikawa and M. Tanno, ”Coordinated multipoint transmission/reception techniques for LTE-Advanced,” IEEE Wireless Commun., vol. 17, pp. 26-34, June 2010.
L. Daewon, S. Hanbyul, B. Clerckx, E. Hardouin, D. Mazzarese, S. Nagata and K. Sayana, ”Coordinated multipoint transmission and reception in LTE-advanced: deployment scenarios and operational challenges,” IEEE Commun. Magazine, vol. 50, no. 2, pp. 148-155, 2012.
3. T. 36.819, ”Coordinated multi-point operation for LTE physical layer aspects, Release 11,” December 2011.
O. El Ayach, S. Peters and R. Heath, ”The feasibility of interference alignment over measured MIMO-OFDM channels,” IEEE Trans. on Vehicular Technol., vol. 59, no. 9, pp. 4309 - 4321, 2010.
W.-L. Chen, I.-H. Huang and T.-S. Lee, ”Mixed criteria interference alignment-aided transceiver design in uplink coordinated multipoint systems,” in IEEE Int. Symp. on Intelligent Signal Process. and Commun. Systems, November 2012.
K. Gomadam, V. R. Cadambe and S. A. Jafar, ”A distributed numerical approach to interference alignment and applications to wireless interference networks,” IEEE Trans. on Inform. Theory, vol. 57, no. 6, pp. 3309-3322, June 2011.
C. Wilson and V. Veeravalli, ”A convergent version of the Max SINR algorithm for the MIMO interference channel,” IEEE Trans. on Wireless Commun., vol. 12, no. 6, pp. 2952-2961, June 2013.
F. Shu, X. H. You, M. Wang, Y. B. Han, Y. Li and W. X. Sheng, ”Hybrid interference alignment and power allocation for multi-user interference MIMO channels,” Science China Inform. Sciences, vol. 56, no. 4, pp. 1-9, April 2013.
V. R. Cadambe and S. A. Jafar, ”Interference alignment and degrees of freedom of K-user interference channel,” IEEE Trans. on Inform. Theory, vol. 54, no. 8, pp. 3421-3441, August 2008.
K. S. Gomadam, V. R. Cadambe and S. A. Jafar, ”Approaching the capacity of wireless networks through distributed interference alignment,” in IEEE GLOBECOM, 2008.
M. Guillaud, D. T. Slock and R. Knopp, ”A practical method for wireless channel reciprocity exploitation through relative calibration,” in Eigth Int. Symp. on Signal Process. and Its Applications, 2005.
I. F. Akyildiz, D. M. Gutierrez-Estevez and E. C. Reyes, ”The evolution to 4G cellular systems: LTE-Advanced,” Physical Communication, vol. 3, no. 4, p. 217–244, 2010.
A. Rakhmania, P.-Y. Tsai and O. Setyawati, ”Combined per-user SLNR and SINR criterions for interference alignment in uplink coordinated multi-point joint reception,” in Asian Pacific Signal and Inform. Process. Assoc. Annual Summit and Conf. (APSIPA ASC), Siem Reap, 2014.
M. Sadek, A. Tarighat and A. H. Sayed, ”A leakage-based precoding scheme for downlink multi-user MIMO channels,” IEEE Trans. on Wireless Commun., vol. 6, no. 5, pp. 1711 - 1721, 2007.
G. H. Golub and C. F. V. Loan, Matrix computations (4th Edition), Baltimore, Maryland: The John Hopkins University Press, 2013.
J. Wilkinson, The algebraic eigenvalue problem, New York: Oxford University Press, 1965.
Z.-Y. Huang and P.-Y. Tsai, ”Efficient implementation of QR decomposition for gigabit MIMO-OFDM systems,” IEEE Trans. on Circuits and Syst., vol. 58, no. 10, pp. 2531-2542, 2011.
R. Brent, F. Luk and C. Van Loan, ”Computation of the singular value decomposition using mesh-connected processors,” J. of VLSI and Computer Systems, vol. 1, no. 3, pp. 242-270, 1983.
Y. Liu, C. Bouganis, P. Cheung, P. Leong and S. Motley, ”Hardware efficient architectures for eigenvalue computation,” in Design, Automation and Test in Europe (DATE), Munich, 2006.
A. Lopez-Parrado and J. Velasco-Medina, ”Efficient systolic architecture for Hermitian eigenvalue problem,” in IEEE 4th Colombian Workshop on Circuits and Syst. (CWCAS), Barranquilla, 2012.
J. Guerrero-Ramirez, J. Velasco-Medina and J. Arce-Clavijo, ”Hardware design of an eigensolver based on the QR method,” in IEEE Fourth Latin American Symp. on Circuits and Syst. (LASCAS), Cusco, 2013.
C. D. Meyer, Matrix analysis and applied linear algebra, SIAM, 2000.
P. K. Meher, J. Valls, T.-B. Juang, K. Sridharan and K. Maharatna, ”50 Years of CORDIC: Algorithms, Architectures, and Applications,” IEEE Trans. on Circuit and Syst. -I: Regular Papers, vol. 56, no. 9, pp. 1893-1907, 2009.

指導教授

蔡佩芸(Pei-Yun Tsai)

審核日期

2015-1-15

推文