臺灣博碩士論文加值系統

In downlink transmission, multicell cooperation is a powerful method to mitigate intercell interference, and transmit diversity gain is also improved through cooperation. When one of the BS suffers from large scale fading like shadow fading and fails to transmit the signal to users, the cooperative base stations (BSs) can substitute for transmitting the signal to those users. Obviously the cooperative BSs provide spatial diversity in the cooperative systems. It is especially good to improve the bit error rate (BER) performance when BSs suffer from large scale fading. However, those cooperative BSs need to share all the channel state information (CSI) in the downlink channel, and it would be a heavy loading of the system.
Now, we propose a method to lower that loading for two cooperative BSs in multiuser multiple input and multiple output (MIMO) downlink communication systems. The proposed
method only shares one real value to represent the complex channel matrix for each user and optimizes the power allocation through that value. The loading to share CSI is reduced to 1/2NrNt. BER of traditional case 1 is 10e−2 when signal to noise ratio (SNR) = 0 dB without cooperation, but BER of the proposed system can be reduced to 10e−4.5 when SNR = 0 dB through cooperation. The proposed system has the better BER performance than traditional systems without cooperation, so it could save more transmitted power in the same BER performance
and reach higher cell coverage.
In simulation results, two BSs in the proposed system and compared systems use the same MIMO precoding algorithm to maximize the effective channel diagonal elements or directly transmit the signal without precoding. The differences between these systems are cooperation conditions and power constraints. To enhance the BER performance, we use maximum likelihood detection (MLD) at the receivers in the proposed system, and the compared systems equitably use MLD at the receivers.
Simulations are based on the MIMO Rayleigh fading channel model with white Gaussian noise. The elements in the channel matrix are assumed i.i.d. complex Gaussian random variables with zero mean and variances are equal to 0.5 per dimension. From the simulation results, we can see that the proposed system is especially good in BER performance. The proposed system has 4dB SNR gain than conventional cooperative case 2 under the per BS power constraints when BER = 10e−3.

在下行通道傳輸中，多小區合作是一種很有利的方去去減輕小區間的訊號干擾，透過合作傳送分集增益也可得到改善。當其中有一個基地台，因為大範圍衰減而無法傳送訊號給使用者時，合作的基地台就能代替傳輸訊號給這些使用者。這些合作的基地台顯然地在合作式系統裡提供了空間的多樣性。當基地台受到大範圍衰減影響時，這對位元錯誤率會有特別好的改善。然而這些合作的基地台必須分享彼此間的下行通道狀態訊息，這可能會是一個系統裡沉重的負荷。
現在我們提出了一個方法去降低這個負荷在兩個合作的基地台多個使用者多輸入多輸出下行傳輸系統。我們提出的方法，對每個使用者只需要去分享一個實值去代替整個複數通道矩陣，並且根據這個值去作功率分配最佳化。分享通道狀態訊息的負荷降低了2NrNt倍。當信噪比是0分貝時，沒有合作的traditional case 1位元錯誤率是10e-2，但當信噪比是0分貝時，透過合作提出的系統的位元錯誤率是10e-4.5。提出的系統在位元錯誤率的性能比傳統沒有合作的系統更好，所以這可以節省更多傳輸功率和小區覆蓋範圍。
在模擬中，兩個基地台在提出的系統裡和比較的系統裡，使用相同的多輸入多輸出預編碼演算法去極大有效通道矩陣中的對角線值，或是不使用預編碼直接傳送訊號。這些系統間的差異在合作的情況以及功率限制的條件。為了增進位元錯誤率的性能，在提出的系統裡，我們在接收端使用最大似然檢測法，比較的系統也平等地在接收端使用最大似然檢測法。
模擬是基於多輸入多輸出瑞利衰弱信道加上高斯雜訊。在矩陣通道裡的元素是相互獨立並具有相同分配的複數高斯隨機值，每個維度的平均值是0且變異數是0.5。從模擬結果中，提出的系統在位元錯誤率的性能特別好。當位元錯誤率是10e-3，在基地台功率限制下提出的系統比起conventional cooperative case 2有4分貝的信噪比增益。

Abstract i
1 Introduction 1
1.1 Overview of MIMO Communication Systems . . . . . . . . . . . . . . . . . 1
1.1.1 Open-Loop MIMO Communication Systems . . . . . . . . . . . . . 1
1.1.2 Closed-Loop MIMO Communication Systems . . . . . . . . . . . . 1
1.2 Motivation of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Multiple-Input Multiple-Output (MIMO) Communication Systems 7
2.1 MIMO Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.3 Spatial Multiplexing . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 MIMO Decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 Singular Value Decomposition (SVD) . . . . . . . . . . . . . . . . . 11
2.2.2 Geometric Mean Decomposition (GMD) . . . . . . . . . . . . . . . 13
2.3 MIMO Detections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) Detections
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.2 QR Based Successive Interference Cancelation (SIC) Detection . . . 15
2.3.3 Sphere Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Multiuser MIMO Downlink Systems 19
3.1 Multiuser MIMO Downlink Mathematical System Model . . . . . . . . . . . 20
3.2 Multiuser Nonlinear Processing Transmission . . . . . . . . . . . . . . . . . 20
3.2.1 Tomlinson-Harashima Precoding (THP) for Multiuser MIMO Downlink
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Multiuser Linear Processing Transmission . . . . . . . . . . . . . . . . . . . 23
3.3.1 Channel Inversion (CI) and Regularized Channel Inversion (RCI) . . 23
3.3.2 Block Diagonal Algorithm . . . . . . . . . . . . . . . . . . . . . . . 25
3.3.3 Generalized Zero-Forcing Channel Inversion (GZI) . . . . . . . . . . 26
4 Cooperative Multicell Multiuser MIMO Downlink Systems 29
4.1 Cooperative MIMO in Cellular Systems . . . . . . . . . . . . . . . . . . . . 29
4.1.1 Relay Based Cooperative Transmission . . . . . . . . . . . . . . . . 29
4.1.2 Multicell Cooperative Transmission . . . . . . . . . . . . . . . . . . 30
4.2 Interference Coordination and Multicell Cooperation . . . . . . . . . . . . . 31
4.3 Cooperative Downlink Multicell Multiuser MIMO Mathematical System Model 32
4.4 SVD based Multicell Transmission . . . . . . . . . . . . . . . . . . . . . . . 33
4.4.1 Joint Power Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.4.2 Scaled Suboptimal Power Allocation . . . . . . . . . . . . . . . . . 35
4.4.3 Grouped Suboptimal Power Allocation . . . . . . . . . . . . . . . . 36
4.5 Other Cooperative Transmission Algorithms with per BS Power Constraints . 37
5 Proposed Cooperative Multicell Multiuser MIMO Downlink Systems 41
5.1 Proposed Mathematical System Model . . . . . . . . . . . . . . . . . . . . . 41
5.2 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.2.1 Block Diagonal Algorithm forM1 andM2 . . . . . . . . . . . . . . 43
5.2.2 MIMO Precoding Algorithm for T1 and T2 . . . . . . . . . . . . . . 44
5.2.3 Power Allocation Algorithm for P1 and P2 . . . . . . . . . . . . . . 47
6 Simulation Results and Discussions 65
6.1 Simulation Results and Discussions . . . . . . . . . . . . . . . . . . . . . . 65
7 Conclusions and FutureWorks 77
7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
7.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

[1] J.-C. Guey and L. Larsson, “Modeling and evaluation of MIMO systems exploiting
channel reciprocity in TDD mode,” in Proc. IEEE VTC ’04, vol. 6, Sep. 2004, pp. 4265–
4269.
[2] S. Leach, “Singular value decomposition – a primer,” Unpublished Manuscript, Department
of Computer Science, Brown University, Providence, RI, USA.
[3] E. Baccarelli and M. Biagi, “A water-filling based approach for power allocation for
multiple-antenna Rayleigh flat fading systems with partially coherent detection,” in
Proc. IEEE VTC ’02, vol. 1, 2002, pp. 92 – 96.
[4] Y. Jiang,W. Hager, and J. Li, “The geometric mean decomposition,” Linear Algebra and
its Applications, vol. 396, pp. 373 – 384, Feb. 2005.
[5] J.-K. Zhang, A. Kavcic, and K. M. Wong, “Equal-diagonal QR decomposition and its
application to precoder design for successive-cancellation detection,” IEEE Trans. Inform.
Theory, vol. 51, pp. 154 –172, Jan. 2005.
[6] W. Liu, S. Ng, and L. Hanzo, “Multicell cooperation based SVD assisted multi-user
MIMO transmission,” in Proc. IEEE VTC ’09, Apr. 2009, pp. 1 –5.
[7] A. Tolli, M. Codreanu, and M. Juntti, “Linear cooperative multiuser MIMO transceiver
design with per BS power constraints,” in Proc. IEEE ICC ’07, Jun. 2007, pp. 4991
–4996.
[8] R. Zhang, “Cooperative multi-cell block diagonalization with per-base-station power
constraints,” IEEE J. Select. Areas. Commun., vol. 28, no. 9, pp. 1435 –1445, Dec. 2010.
[9] R. Zhang, “Cooperative multi-cell block diagonalization with per-base-station power
constraints,” in Proc. IEEE WCNC ’10, Apr. 2010, pp. 1 –6.
[10] C. Shannon, N. Petigara, and S. Seshasai, “A mathematical theory of communication,”
Bell System Technical Journal, vol. 27, pp. 379–423, 1948.
[11] S. Alamouti, “A simple transmit diversity technique for wireless communications,” IEEE
J. Select. Areas. Commun., vol. 16, no. 8, pp. 1451 –1458, Oct. 1998.
[12] G. H. Golub and C. F. V. Loan, Matrix Computations. The Johns Hopkins University
Press, 1996.
[13] E. Larsson, “MIMO detection methods: How they work [lecture notes],” IEEE Signal
Process. Mag., vol. 26, no. 3, pp. 91 –95, May 2009.
[14] P.-L. Chiu and Y.-H. Huang, “A scalable MIMO detection architecture with non-sorted
multiple-candidate selection,” in Proc. IEEE ISCC ’09, May 2009, pp. 689 –692.
[15] Q. H. Spencer, C. B. Peel, A. L. Swindlehurst, and M. Haardt, “An introduction to the
multi-user MIMO downlink,” pp. 60–67, Oct. 2004.
[16] M. Costa, “Writing on dirty paper,” vol. 29, no. 3, pp. 439 – 441, May 1983.
[17] G. Caire and S. Shamai, “On the achievable throughput of a multi-antenna Gaussian
broadcast channel,” vol. 43, no. 7, pp. 1697–1706, Jul. 2003.
[18] H. Weingarten, Y. Steinberg, and S. Shamai, “The capacity region of the Gaussian
MIMO broadcast channel,” vol. 52, no. 9, pp. 3936–3964, Sep. 2006.
[19] C. B. Peel, B. M. Hochwald, and A. L. Swindlehurst, “A vector-perturbation technique
for near-capacity multiantenna multiuser communication - part II: Perturbation,” vol. 53,
no. 3, pp. 537–544, Mar. 2005.
[20] Q. Spencer and M. Haardt, “Capacity and downlink transmission algorithms for a multiuser
MIMO channel,” in Proc. IEEE ACSSC ’02, vol. 2, Nov. 2002, pp. 1384 – 1388.
[21] H. Sung, S.-R. Lee, and I. Lee, “Generalization of channel inversion algorithms for
multiuser MIMO downlink systems,” in Proc. IEEE ICC ’08, May 2008, pp. 3350–
3354.
[22] C. Peel, “On dirty-paper coding,” IEEE Signal Process. Mag., vol. 20, no. 3, pp. 112 –
113, May 2003.
[23] U. Erez, S. Shamai, and R. Zamir, “Capacity and lattice strategies for canceling known
interference,” IEEE Trans. Inform. Theory, vol. 51, no. 11, pp. 3820 – 3833, Nov. 2005.
[24] M. Tomlinson, “New automatic equaliser employing modulo arithmetic,” Electronics
Letters, vol. 7, no. 5, pp. 138 –139, 1971.
[25] H. Harashima and H. Miyakawa, “Matched-transmission technique for channels with
intersymbol interference,” IEEE Trans. Commun., vol. 20, no. 4, pp. 774 – 780, Aug.
1972.
[26] C. Windpassinger, R. Fischer, T. Vencel, and J. Huber, “Precoding in multiantenna and
multiuser communications,” IEEE Trans. Wireless Commun., vol. 3, no. 4, pp. 1305 –
1316, Jul. 2004.
[27] W. Ho, T. Quek, and S. Sun, “Decentralized base station processing for multiuser MIMO
downlink CoMP,” in Proc. IEEE VTC ’10, May 2010, pp. 1 –5.
[28] M. Joham, K. Kusume, W. Utschick, and J. Nossek, “Transmit matched filter and transmit
wiener filter for the downlink of FDD DS-CDMA systems,” in Proc. IEEE PIMRC
’02, vol. 5, Sep. 2002, pp. 2312 – 2316.
[29] W. Liu, L.-L. Yang, and L. Hanzo, “SVD assisted joint transmitter and receiver design
for the downlink of MIMO systems,” in Proc. IEEE VTC ’08, Sep. 2008, pp. 1 –5.
[30] J. Zhang, R. Chen, J. Andrews, and R. Heath, “Coordinated multi-cell MIMO systems
with cellular block diagonalization,” in Proc. IEEE ACSSC ’07, Nov. 2007, pp. 1669
–1673.
[31] A. Tolli, M. Codreanu, and M. Juntti, “Linear multiuser MIMO transceiver optimization
in cooperative networks,” in Proc. IEEE CHINACOM ’07, Aug. 2007, pp. 513 –517.
[32] S. Lin, W. Ho, and Y.-C. Liang, “Block-diagonal geometric mean decomposition (BDGMD)
for multiuser MIMO broadcast channels,” in Proc. IEEE PIMRC ’06, Sep. 2006,
pp. 1 –5.
[33] S. Lin, W. Ho, and Y. chang Liang, “Block diagonal geometric mean decomposition
(BD-GMD) for MIMO broadcast channels,” IEEE Trans. Wireless Commun., vol. 7,
no. 7, pp. 2778 –2789, Jul. 2008.