臺灣博碩士論文加值系統

合作式通訊是一種可以實現空間分集以對抗通道衰落的技術，他可以讓一根天線的終端藉由網路內其他一個或是多個終端的合作來形成虛擬的正列天線，因此可以形成空間分集的效益。對於這個研究中，我們提出了一個在合作式通訊網路系統下多重使用者的傳輸策略，藉由我們的傳輸模式可以讓多個使用者可以藉由合作式的中繼端（Relays）傳輸來同時分享無線通訊中的資源。有別於一般的合作式多重使用者的系統在中繼節點多使用一組時間槽（time-slot）的傳送方法，我們在所有的中繼節點做編碼（precoding）之後一口氣傳送所有訊號給個別的接收端在做（decoding）來增加傳送頻寬效率。我們所設計的兩組編碼是依據是在中繼節點都已知通道資訊的情況之下，藉由破零（zero-forcing）來消除多重路徑干擾（MAI）以及考慮最大訊雜比（SNR）之下藉由中繼節點做有效的能源分配限制（Power constraint）所設計。模擬結果顯示，相對於傳統的傳輸策略或是直接傳輸沒有做合作式通訊的情況下，我們所研究出來的方法可以有效的改善儲運耗損容量（Outage Capacity）

The cooperative communication is one of technologies which can explore the space diversity to resist fading channel. The spatial diversity is achieved by allowing various terminals behaving or a virtual antenna array and forwarding signal for a source terminal in cooperative manner. Under the existence of multiple sources, resource allocation to each source user is even more crucial to enhance the system performance and achieve higher diversity gain. In this work, we proposed a multiuser relaying strategy for a cooperative network with multiple sources sharing the radio resource provided by the cooperative relays simultaneously. Different from the existing work, the set of relays forwards signals of all source users over a common channel to raise spectral efficiency. With full channel information available at relays, the set of sub-optimal precoders and decoders was proposed in terms of maximal the average SNR over all users, subject to eliminating the multiple access interference (MAI) at each destination and satisfying total power constraint among all relays. It shows from the simulation results that, compared with the conventional cooperative strategy and direct transmission, the proposed scheme provides pronounced improvement on the outage capacity.

Keywords: user cooperation, multiple access, resource allocation

Table of Content

Abstract
Content
List of Figures

Abstract
Content
List of Figures

Chapter 1: Introduction
1.1 Motivation 1
1.2 Related Work 2
1.3 Contribution. 3
1.4 Outline 4

Chapter 2:Literature Survey
2.1Wireless Fading Channel
2.2Background of Cooperative Communication 5
6
2.3 Zero-Frocing Based Gain Allocation for Wireless Multiuser Network 11
2.3.1 Optimization based on Zero-Forcing Criterion 12
2.3.2 Optimization of Relay Gain 13
2.4 Cooperative Distributed Multiuser MMSE Relaying in Wireless Ad-Hoc Network 16

Chapter 3: Multiple Access Schemes and System Model
3.1 Direct Transmission and Conventional Cooperation 19
3.2 System Model of the Proposed Scheme 21
3.3Proposed Analysis 26

Chapter 4 : Joint Design of the Precoders and Decoders
4.1 Zero-Forcing Decoder Design 28
4.2 Optimal Design of Decoder in terms of Precoding Factors 31
4.3 Sub-Optimal Design of Precoders 32
4.3.1 Precoder Optimization based on Approximated SNR 33
4.3.2 Precoder Optimization based on Lower Bound of SNR 36
4.4 Outage Analysis of the Proposed Schemes 38

Chapter 5: Computer Simulations
5.1 Simulation environment 41
5.2 Simulation Results for Precoder Optimization based on Approximated SNR 42
5.3 Suboptimal Precoders based on the Lower Bound of SNR 45
5.4 Comparisons of Two Suboptimal Precoders 49

Chapter 6: Conclusion
52
Reference 53

[1].J. Yang and S. Roy, “Joint transmitter-receiver optimization for multi-input multi-output systems with decision feedback, ” IEEE Trans. Inform. Theory, vol. 40,pp. 1334-1347, Sept.1994
[2].A. Scaglione, P.Stoica, S. Barbarossa, G. B. Giannakis, and H. Sampath, “Opti-
Mal designs for space-time linear procoders and decoders,” IEEE Trans. Signal Processing, vol. 50, pp. 1051-1064, May 2002.
[3].C.N. Chuah, D. N. C. Tse, J. M. Kahn, and R. A. Valenzuela, “Capacity scaling in MIMO wireless systems under correlated fading,” IEEE Transactions on Information Theory, vol.48,pp.637-650,2002.
[4].D. Gesbert, H. Bolcskei, D. A. Gore, and A. J. Paulraj, “Outdoor MIMO wireless channels:models and performance prediction,” IEEE Transactions on Commun-
Ications, vol.50,pp. 1926-1934,2002.
[5].A. Sendonaris, E. Erkip and B. Aazhang, “User Cooperation Diversity–Part I: System Description” and “User Cooperation Diversity–PartII: Implementation Aspects and Performance Analysis,” IEEE Trans. Commun., vol. 51, no. 11, Nov. 2003.
[6].J. Laneman, D. Tse, and G. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Trans. Inform. Theory, vol. 50, no. 12, pp. 3062–3080, Dec. 2004.
[7].Y.-W. Hong, W.-J. Huang, F.-H. Chiu and C.-C. Jay Kuo, “Cooperative
[8].Communications for Resource Constrained Wireless Networks”, to appear in IEEE Signal Processing Magazine, May 2007
[9]. I. Hammerstrom, M. Kuhn and A. Wittneben, “Channel adaptive scheduling for cooperative relay networks,” in Proc. of Vehicular Technology Conf. (VTC), Vol.4, pp.2784–2788, Sep. 2004.
[10]. I. Hammerstrom, J. Zhao,and A. Wittneben, “Temporal fairness enhanced scheduling for cooperative relaying networks in low mobility fading environments,”in Proc. on IEEE 6th Workshop on Signal Processing
Advances in Wireless Communications, pp. 525V529, 2005
[11]. A. Goldsmith, Wireless communication. Cambridge University Press,2005.
[12]. A. K. Sadek, W. Su, and K. J. R. Liu, “Multi-node cooperative communications
in wireless networks,” IEEE Transactions on Signal Processing, vol. 55, pp. 341-355, January 2007.
[13].W. Su, A. K. Sadek, and K. J. R. Liu, “Cooperative communication protocols in wireless networks: performance analysis and optimum power allocation,” Wireless Personal Communications, vol. 44, pp. 181-217, January 2008.
[14].S. Berger and A. Wittneben, “Cooperative Distributed Multiuser MMSE Relaying in Wireless Ad-Hoc Networks,” in Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, CA, Oct./Nov. 2005.
[15]. A. Wittneben, “Coherent Multiuser Relaying with Partial Relay Cooperation,” in Proc. IEEE WCNC, Las Vegas, USA, Apr. 2006.
[16]. Z. Yi, I.M. Kim, “ Joint optimization of relay-precoders and decoders with partial channel side information in cooperative networks,” in IEEE Journal on Selected Areas in Communications, 5vol.24, no.2,pp447–458,Feb. 2007
C.Esli, Stefan Berger, and A. Wittneben, “Optimizing Zero-forcing based gain
[17].C.Esli, Stefan Berger, and A. Wittneben, “Optimizing Zero-forcing based gain allocation for wireless multiuser networks,” in Proc. of IEEE Inter. Conf. on Commun. 2007, June 2007, pp. 5825-5830.

[18]. D. Bertsekas and R. Gallager , “Data Networks”, Prentice-Hall, International Editions, 1987.
[19]. D. G. Luenberger , “Linear and Nonlinear Programming”, Addison- Wesley, 1984.
[20]. S. Boyd and L. Vandenberghe, “Convex Optimization”. Cambridge University Press, 2004.
[21]. N. Khajehnouri and A. H. Sayed, “A distributed MMSE relay strategy for wireless sensor networks,” in IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Columbia University, NY, Jun. 2005.