臺灣博碩士論文加值系統

在合作式通訊 ( Cooperative Relay Network ) 的系統架構下，將分散式時空方塊碼( Distributed Space Time Block Code) 編碼方法應用在放大再傳送(Amplified-and-Forward) 的合作方式下，討論如何利用領航信號(Pilot)的傳送，讓接收端(Destination)去做通道的估計(Channel Estimation)；我們討論了兩種方法：Source-Assisted (SA) 和 Source-and-Relay-
Assisted (SRA)。在SA這個方法裡，我們只在Source端傳送領航信號(Pilot)，Relay只需要將Source傳來的領航信號(Pilot)和數據信號(Data)放大再傳送，如此一來，接收端可以藉由收到的領航信號(Pilot)去估計從Source端到Destination端的等效通道 (Effective Channel) ; 在SRA這個方法裡，Relay除了要幫忙傳送Source的信號外，還要額外傳送自己的領航信號(Pilot)給接收端，Destination端不但可以估計從Source端到Destination端的等效通道，還可以額外估計Relay端到Destination端的通道，進而知道Source端到Relay端的通道。
對於SA這個方法而言，我們還討論了在固定的Power之下，如何分配Power給Pilot和Data，使得Destination在偵測信號的時候，可以達到最小的錯誤率，由推導的式子結果可以看出，在Relay端的資源分配上，領航信號需要比數據信號多出更多的 Power。
在模擬的分析結果裡，由於SRA 需要用更大的Block傳送資料來達到相同的數據速率Data Rate，所以效能 (Performance) 的表現來的比SA來的差；我們還驗證了資源最佳化分配(Optimal Power Allocation) 的好處，更觀察到在Relay端做資源分配的重要性遠大於Source端 ; 除此之外，比較了不同領航信號 (Pilot) 的個數對通道估計的影響。 最後，比較了SA 及 SRA 在均方誤差(Mean Square Error) 上的表現，更驗證了前面的效能結果。

In this thesis, we study the effect of channel estimation on the performance of distributed space-time coding (DSTC) in amplify-and-forward (AF) cooperative networks. The transmission is divided into two phases. In phase I, the source first transmits a block of data to the relays after which each relay will retransmit an encoded block of data to the destination in phase II. For AF networks, the signal transmitted by the source experiences a combined channel that consists of the multiplication of channel coefficients on the source-to-relay and the relay-to-destination links. The combined channel must be estimated at the destination in order to perform coherent detection. Based on the concept of TDM training, we propose two pilot insertion methods: the Source-Assisted (SA) Training Method and the Source-and-Relay Assisted (SRA) Training Method. In the SA method, pilot is generated only at the source while the relay amplifies-and-forwards the source's pilot as if it would a data symbol. In the SRA method, an additional pilot is generated by each relay in addition to relaying the source's pilot. The SRA allows the destination to
estimate explicitly the relay-to-destination channel, but requires a larger block size in order to achieve a desired data rate. Given a total power constraint, a tradeoff exists in the amount of power that is allocated between
pilot and data symbols. Therefore, by minimizing the pairwise error probability of a block fading approximation model, we also derive the optimal power allocation over pilot and data symbols. The performance of the proposed pilot insertion and power allocation strategies are compared through computer simulations.

1 Introduction 1
2 System Model 5
2.1 Distributed Space-Time Coding . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Pilot Power and Placement in DSTC Systems . . . . . . . . . . . . . . . . . 8
2.2.1 Source-Assisted (SA) Training Method . . . . . . . . . . . . . . . . . 8
2.2.2 Source-and-Relay-Assisted (SRA) Training Method . . . . . . . . . . 11
3 Channel Estimation and Symbol Detection 13
3.1 Channel Estimation for SA Training . . . . . . . . . . . . . . . . . . . . . . 13
3.2 Channel Estimation for SRA Training . . . . . . . . . . . . . . . . . . . . . . 17
4 Power Allocation over Pilot and Data Symbols 21
5 Numerical Simulations and Performance Comparisons 27
5.1 SA and SRA without Power Allocation . . . . . . . . . . . . . . . . . . . . . 27
5.2 Optimal Power Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.3 Eect of the Number of Pilots . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.4 Mean-Square-Error (MSE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6 Conclusion 42
7 Appendix 44
7.1 Simulation of Rayleigh Faded Mobile-to-Fixed Communication Channel (Slow
Fading) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
7.2 Simulation of Rayleigh Faded Mobile-to-Mobile Communication Channels
(Fast Fading) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

[1] Y.-W. Hong, W.-J. Huang, F.-H. Chiu, and C. Jay Kuo, “Cooperative communications
in resource-constrained wireless networks,” IEEE Signal Processing Mag. , pp. 47–57,
May 2007.
[2] J. Laneman, D. Tse, and G. Wornell, “Cooperative diversity in wireless networks: E-
cient protocols and outage behavior,” IEEE Trans. Inform. Theory, vol. 50, no. 12, Dec.
2004, pp. 3062–3080.
[3] M. Janani, A. Hedayat, T. E. Hunter, and A. Nosratinia, “Coded cooperation in wireless
communications: Space-time transmission and iterative decoding,” IEEE Trans. Signal
Process., vol. 52, no. 2, pp. 362–37, Feb. 2004 .
[4] G. Kramer M. Gastpar, and P. Gupta, “Cooperative strategies and capacity theorems for
relay networks, IEEE Trans. Inform. Theory, vol. 51, no. 9, Sept. 2005, pp. 3037–3063.
[5] Z. Yi and I.-M. Kim, “Joint optimization of relay-precoders and decoders with partial
channel side information in cooperative networks,” IEEE J. Sel. Areas Commun., vol. 24,
no. 2, pp. 447–458, Feb. 2007.
[6] R. U. Nabar, H. Bolcskei and F. W. Kneubuhler, “Fading relay channels: Performance
limits and space-time signal design” IEEE J. Select. Areas Commun., vol. 22, no. 6, pp.
1099–1109, Aug. 2004.
[7] Y. Jing and B. Hassibi, “Distributed space-time coding in wireless relay networks,” IEEE
Trans. Wireless Commun., vol. 5, no. 12, pp. 3524–3536, Dec. 2006.
[8] S. Yiu, R. Schober, and L. Lampe, “Distributed space time block coding” IEEE Trans.
Commun., vol. 54, no. 7, July 2006
[9] B. Sirkeci-Mergen and A. Scaglione, “Randomized space-time coding for distributed co-
operative communication,” IEEE Trans. Signal Process., vol. 55, no. 10, pp. 5003–5017,
Oct. 2007.
[10] A. Bletsas, A. Khisti, D.P. Reed, and A. Lippman, “A simple cooperative diversity
method based on network path selection,” IEEE J. Sel. Areas Commun., vol. 24, no. 3,
pp. 659–672, Mar. 2006.
[11] S.M. Alamouti, “A simple transmit diversity technique for wireless communications,”
IEEE J. Sel. Areas in Commun., vol. 16, no. 8, pp. 1451–1458, Oct. 1998.
[12] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space time block codes from orthog-
onal designs” IEEE Trans. Inform. Theory, vol. 45, no. 5, pp. 1456–1467, July 1999.
[13] V. Tarokh, N. Seshadri, and A. R. Calderbank, “Space time codes for high data rate
wireless communication: Performance criterion and code construction” IEEE Trans. In-
form. Theory, vol. 44, no. 2, Mar. 1998.
[14] B. L. Hughes, “Dierential space-time modulation” IEEE Trans. Inform. Theory, vol.
46, no. 7, Nov. 2000
[15] W.-K. Ma, B.-N. Vo, T. N. Davidson, and P.-C. Ching, “Blind ML detection of orthog-
onal space-time block codes: Ecient high-performance implementations”, IEEE Trans.
Sign[16] M. Dong, L. Tong and B. M. Sadler “Optimal insertion of pilot symbols for transmissions
over time-varying at fading channels” IEEE Trans. Signal Process., vol. 52, no. 5, May
2004
[17] B. Hassibi and B.M. Hochwald, “How much training is needed in a multiple-antenna
wireless link?”, IEEE Trans. Inform. Theory, vol.49, no.10, Apr. 2003, pages 951–964.
[18] C. Budianu and L. Tong, “Channel estimation for space-time orthogonal block codes”
IEEE Trans. Wireless Commun., vol. 50, no. 10, Oct. 2002
[19] C. S. Patel and G. L. Stuber, “Channel estimation for amplify and forward relay based
cooperation diversity systems” IEEE Trans. Commun., vol. 6, no. 6, June 2007
[20] W. C. Jakes, Microwave Mobile Communications, 2nd ed. Piscataway, NJ: IEEE Press,
1994.
[21] M. Patzold, Mobile Fading Channels. West Sussex, England: John Wiley Sons, 2002.
[22] C. S. Patel, G. L. Stuber, and T. G. Pratt, “Simulation of Rayleigh faded mobile-to-
mobile communication channels,” IEEE Trans. Commun, vol. 53, no. 11, pp. 1876–1884,
Nov. 2005.
[23] L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time-series Analy-
sis. Reading, MA: Addison-Wesley, 1991.al Process. , vol. 54, no. 2, pp. 738–751, Feb., 2006.