臺灣博碩士論文加值系統

在多用戶無線通訊系統中，個別使用者間都希望傳輸資料到所對應的接收器，若干擾訊號沒有有效的處理，將會導致用戶的總和傳輸速率效能下降。近幾年干擾對齊的概念被提出來克服干擾的問題並且達到最大的總傳輸速率，此種方法是利用多天線所提供的空間上的維度來將所有干擾訊號對齊在同一個干擾子空間中。然而過去有許多干擾對齊方法的文獻，但是在這一些文獻中沒有考慮到使用者欲接收到訊號的強度，也沒考慮雜訊的影響。因此在某些情況下，即使干擾訊號完美對齊在一干擾子空間中，使用者的訊號與干擾雜訊比(Signal to interference and noise ratio, SINR)仍會非常的低。面對此問題，本論文提出一個聯合式預編碼器與解碼器設計的干擾對齊方法，最主要的最佳化問題是考慮最差使用者接收器的訊號與干擾雜訊比，並且使其最大化。然而此問題為一非凸型最佳化問題。因此，我們利用K.K.T.條件推導出解碼器的最佳化條件，接著提出二分法、下界近似法與訊號與干擾雜訊比近似法三種方法來求得最佳的解碼器矩陣。而預編碼器的設計，我們藉著半正定規劃(Semi-definite programming, SDP)與秩數放寬(Rank relaxation)的方法，將問題轉換為凸最佳化問題來求得最佳的預編碼矩陣。最後，根據模擬出的結果得知，無論多天線所提供自由度的多寡，本論文所提出的方法對於最差使用者的訊號與干擾雜訊比皆會比先前文獻[1][2]的方法來的好。

In multiuser interference channels, each transmitter desires to communicate its data to the intended receiver, and the multiuser interference will cause severe performance degradation on the sum rate performance if the interference signals are not appropriately mitigated. Recently, the idea of interference alignment has been emerged to utilize the spatial dimension offered by multiple antennas to overcome the interference problem and achieve the maximum sum rate performance. Although there have been some interference alignment approaches for designing precoders and decoders in recent literatures, they do not consider the desired signal strength from one transmitter to the intended receiver and the noise effect. In some cases, users may have the extremely poor SINR performance even if users’ interference signals are all appropriately aligned. Toward this end, this thesis proposes a novel iterative interference alignment approach which jointly designs the precoder and the decoder by maximizing the worst signal-to-interference and noise ratio (SINR) among users with multiple antennas. In fact, the considered optimization problem is non-convex. Based on the Karush-Kuhn-Tucker (K.K.T.) conditions, we first derive the optimality condition for designing the decoder, and propose three methods, named as bisection approach, lower bound approximation approach and SINR approximation approach, to achieve the optimal solution. For the precoder designs, we resort to the semidefinite programming (SDP) and rank relaxation techniques to transform the optimization problem into a convex form to obtain the optimal solution. Numerical results reveal that our proposed algorithms perform much better than the two existing algorithms in [1] and [2] in the generalized multiuser multi-input multi-output (MIMO) interference channels.

摘要 I
Abstract II
圖目錄 VI
表目錄 VII
第一章 1
簡介 1
1.1干擾管理 1
1.2研究動機 3
1.3論文貢獻 4
1.4論文架構 5
第二章 6
系統模型 6
2.1多用戶多天線干擾通道 6
第三章 10
干擾對齊 10
3.1基本概念 10
3.2對齊條件 11
第四章 14
文獻探討 14
4.1利用對偶性質之分散式干擾對齊[18] 14
4.2交替式最小化之干擾對齊[1] 16
4.3 結合訊號與干擾訊號之干擾對齊[2] 20
第五章 24
結合式預編碼器與解碼器設計 24
5.1預編碼器設計 25
5.1.1二分法 27
5.1.2下界近似法 28
5.1.3訊號與干擾雜訊比近似法 30
5.2預編碼器設計 35
5.2.1二分法 35
5.2.2修正型訊號與干擾雜訊比近似法 37
第六章 40
模擬結果 40
第七章 49
結論 49
附錄A 51
附錄B 53
附錄C 54
附錄D 56
參考文獻 58

[1]S. W. Peters, R. W. Jr. Heath, “Interference Alignment via Alternating Minimization”, in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, Apr. 2009

[2]K. R. Kumar, X. Feng, “An Iterative Algorithm for Joint Signal and Interference Alignment,” Information Theory Proceedings (ISIT), 2010 IEEE International Symposium on, pp. 2293-2297, June 2010


[3]H. Sato, “The Capacity of The Gaussian Interference Channel Under Strong Interference (Corresp.),” Information Theory, IEEE Transactions on, vol. 27, no. 6, pp. 786-788, Nov 1981

[4]N. Bo and A. M. Haimovich, “Interference Subspace Tracking for Network Interference Alignment in Cellular Systems,” Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE, pp. 1-5, Dec. 2009

[5]R. S. Ganesan, T. Weber, and A. Klein, “Interference Alignment in Multi-User Two Way Relay Networks,” Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd, pp. 1-5, May 2011

[6]Z. Jinfan, W. Yongle, Z. Shidong, and W. Jing, “Joint linear transmitter and receiver design for the downlink of multiuser MIMO systems,” Communications Letters, IEEE, vol. 9, no. 11, pp. 991-993, Nov. 2005

[7]R. H. Etkin, D. N. C. Tse, and W. Hua, “Gaussian Interference Channel Capacity to Within One Bit,” Information Theory, IEEE Transactions on, vol. 54, no. 12, pp. 5534-5562
[8]S. Wonjae, L. Namyoon, N. Wonjong, C. Hyun-Ho, B. Clerckx, S. Changyong, and J. Kyunghun, “Hierarchical Interference Alignment for Heterogeneous Networks with Multiple Antennas,” Communications Workshops (ICC), 2011 IEEE International Conference on, pp. 1-6, June 2011

[9]T. Han, K. Kobayashi, “A New Achievable Rate Region for The Interference Channel,” Information Theory, IEEE Transactions on, vol. 27, no. 1, pp. 49-60, Jan 1981

[10]A. H. Madsen, A. Nosratinia, “The Multiplexing Gain of Wireless Networks,” Information Theory, 2005. ISIT 2005. Proceedings. International Symposium on, pp. 065-2069, Sept. 2005

[11]S. Xiaohu, G. Kramer, C. Biao, “A New Outer Bound and the Noisy-Interference Sum–Rate Capacity for Gaussian Interference Channels,” Information Theory, IEEE Transactions on, vol. 55, no. 2, pp. 689-699, Feb. 2009

[12]V. Annapureddy and V. Veeravalli, “Gaussian interference networks: Sum capacity in the low interference regime and new outer bounds on the capacity region,” IEEE Trans. Inf. Theory, Feb. 2008

[13]V. R. Cadambe and S. A. Jafar, “Interference Alignment and Degrees of Freedom of the K-User Interference Channel,” Information Theory, IEEE Transactions on, Vol. 54, Issue 8, pp. 3425-3441, Aug. 2008

[14]G. Tiangao and S. A. Jafar, “Degrees of Freedom of the User MIMO Interference Channel,” Information Theory, IEEE Transactions on, vol. 56, no. 12, pp. 6040-6057, Dec. 2010


[15]H. Du, and T. Ratnarajah, “Robust joint signal and interference alignment for MIMO cognitive radio network,” Wireless Communications and Networking Conference (WCNC), 2012 IEEE, pp. 448-452, April. 2012

[16]B. Guler, and A. Yener, “Interference Alignment for Cooperative MIMO Femtocell Networks,” Global Telecommunications Conference (GLOBECOM 2011), 2011 IEEE, pp. 1-9, Dec. 2011

[17]M. A. Maddah-Ali, A. S. Motahari, and A. K. Khandani, ”Signaling over MIMO Multi-Base Systems: Combination of Multi-Access and Broadcast Schemes,” Information Theory, 2006 IEEE International Symposium on, pp. 2104-2108, July 2006

[18]K. Gomadam, V. R. Cadambe, S. A. Jafar, “A Distributed Numerical Approach to Interference Alignment and Applications to Wireless Interference Networks,” Information Theory, IEEE Transactions on , vol. 57, no. 6, pp. 3309-3322, June 2011

[19]P. Viswanath, D. N. C. Tse, “Sum Cpacity of the Vctor Gaussian Broadcast Channel and Uplink-Downlink Duality,” Information Theory, IEEE Transactions on, vol. 49, no. 8, pp. 1912- 1921, Aug. 2003

[20]Z. Hongyuan, N. B. Mehta, A.F. Molisch, Z. Jin, and D. Huaiyu, “Asynchronous Interference Mitigation in Cooperative Base Station Systems,” Wireless Communications, IEEE Transactions on, vol. 7, no. 1, pp. 155-165, Jan. 2008

[21]S. A. Jafar, S. Shamai, “Degrees of Freedom Region of the MIMO X Channel, ” Information Theory, IEEE Transactions on, vol. 54, pp. 151-170, Jan. 2008

[22]Y. Birk, T. Kol, “Informed-Source Coding-on-Demand (ISCOD) over Broadcast Channels,” INFOCOM ''98. Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE , vol. 3, pp. 1257-1264, Apr. 1998

[23]V. R. Cadambe and S. A. Jafar, “Interference alignment and degrees of freedom of the K-User interference channel,” IEEE Trans. on Inform. Theory, vol. 54, pp. 3425-3441, Aug. 2008.

[24]T. Gou and S. A. Jafar, “Degrees of freedom for the 4 user SIMO interference channel,” in Proc. IEEE GLOBECOM, 2008, pp. 921-925

[25]M. L. Ku, and C. C. Huang, “A complementary codes pilot-based transmitter diversity technique for OFDM systems,”Wireless Communications, IEEE Transactions on, vol. 5, no. 3, pp. 504- 508, March 2006

[26]S. Boyd and L. Vandenberghe, “Convex Optimization,” ser. Course Reader for EE364 (Stanford)/EE236B (UCLA).

[27]R. A. Horn and C. R. Johnson, “Matrix Analysis,” Cambridge, UK: Cambridge University Press, 1996.

[28]R. A. Horn and C. R. Johnson, “Topics in Matrix Analysis,” Cambridge, UK: Cambridge University Press, 1996.

[29]H. Lutkepohl, “Handbook of Matrices,” Wiley, 1997.

[30]J. Dattorro, “Convex optimization and Euclidean distance geometry,” Meboo, 2005.

[31]M. Grant, S. Boyd, and Y. Ye, cvx: Matlab Software for Disciplined Convex
Programming. [Online]. Available: http://www.stanford.edu/~boyd/cvx/