在任何無線通道傳輸中，接收端除了會接收到含有雜訊的訊息外，傳送的過程中還會遇到路徑喪失、重路徑干擾和衰減效應的影響，而衰減程度大小與所在的環境有關。為提升傳輸的速度，藉由架設中繼站來合作協助覆蓋範圍所有的使用者，使傳送品質變好，此方法統稱合作式通訊。中繼站的加入不僅有效增加空間多樣性，設立的成本也比較低。本論文採用中繼協定中的解碼後傳遞作為資料轉傳的技術，並且搭配通道資訊及接收端使用不同多樣性結合的方式(例如: Selection Combining, Maximum Ratio Combining)，也提出類似典型結合的演算法降低錯誤率改善系統效能。
常見的回傳方式為接收端利用通道響應並回傳通道振幅給傳送端，而通道資訊的簡化與有效回傳的演算法也持續被討論。為了更有效率的傳送通道資訊，基於Karhunen-Loève Expnsion (KLE) 的概念提出回傳有效通道響應的估測值至傳送端的方法。經由KL轉換後的係數個數比原始通道響應的數量減少很多，我們將這些KLE係數當作重建通道響應的重要參數，又因為這些參數為高斯分布，所以可以利用高斯量化來減少量化誤差。從模擬結果來看，利用通道資訊的合作式正交頻多工調變與提出的演算法可以達到低傳送負載且低錯誤率的表現。

In any wireless channel transmission, the receiver not only gets the signals with noise, but also suffers path loss, multipath interference, and fading effect during transmission. Different kinds of situations may cause distinct magnitudes of fading. To shorten the time of transmission, setting the relay station can help all the users in the coverage and have better communication quality. This is well-known as the cooperative communication. One advantage of setting the relay station is its low complexity and cost if the relay user is idle. Another is that it increases the spatial diversity. To improve the performance of bit-error rate, this thesis applies decode and forward to send the signal to destination. With the channel state information (CSI) at receiver, we propose combining techniques and give the comparison with the traditional diversity combining like Selection Combining (SC) and Maximum Ratio Combining (MRC).
The receiver makes use the characteristic of channel responses and feeds forward these estimated amplitudes of CRs to the transmitter. It exists some methods about simplifying the CSI and related feed forward/back algorithms. To efficiently feed forward estimated CSI, we use some schemes which feedback estimated CRs to transmitter based on Karhunen-Loève Expansion (KLE). After the KL transform, the number of KLE coefficients is much fewer than the size of CRs. We can feed forward/back these coefficients of KLE such that the other terminal can rebuild CRs. Because these coefficients are of Gaussian distribution, we can further apply the Gaussian Quantization (GQ) to reduce the quantization error. As simulation results show, by applying these algorithms, we can obtain better error performance in cooperative-OFDM systems.

[1] J. N. Laneman, D.N.C. Tse, and G. W. Wornell. “Cooperative diversity in wireless networks: Efficient protocols and outage behavior’’. IEEE Trans. On Inf Theory,50(12):3062-3080,Dec. 2004.
[2] A. K. Sadek, W. su, and K. J. R. liu. “Multinode cooperative communication in wireless networks’’. IEEE Trans. Signal Process.,vol. 55, no. 1 , pp. 341-355, Jan.2007
[3] A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity -part I: System description,” IEEE Trans. Commun., vol. 51, no. 11, pp.1927-1938, Nov. 2003
[4] A. Sendonaris, E. Erkip, and B. Aazhang. “User cooperation diversity. Part ii. Implementation aspects and performance analysis’’. IEEE Trans. on Commun.,51(11):1939-1948,Nove 2003.
[5] A. Ribeiro, X. Cai, and G.B. Giannakis. “Symbol error probabilities for general cooperative links’’. IEEE Trans. on wireless Commun., 4(3):1264-1273, May 2005.
[6] Qian Li, Rose Qingyang Hu,Yi Qian and Geng Wu.” Cooperative communications for wireless networks: techniques and applications in LTE-advanced systems”. IEEE Trans. Vol.19, Apr.2012
[7] T. Wang, A. Cano, G. B. Giannakis, and J. N. Laneman. “High-performance cooperative demodulation with decode-and-forward relays’’. IEEE Trans. on Commun., 55(7):1427-1438, July 2007
[8] Y. Li, and X Huang, “The simulation of Independent Rayleigh Faders,’’ IEEE Trans. Commun.,vol. 50, No. 9,pp.1503-1514,Sept.2002.
[9] A. Kuhne, “throughput analysis of multi-user OFDMA-systems using imperfect CQI
feedback and diversity techniques,” IEEE J. Select. Areas Commun., vol. 26, no. 8,pp. 1440 - 1450, October 2008.
[10] S.Guharoy, N.B. Mehta, “Joint Evaluation of Channel Feedback Schemes, Rate
Adaptation, and Scheduling in OFDMA Downlinks With Feedback Delays,” IEEE
Trans. Veh. Technol., vol. 62, No. 4, pp. 1719-1731, May 2013.
[11] S. Zhou, G. B. Giannakis, “Optimal transmitter eigen-beamforming and space-time block coding based on channel mean feedback,” IEEE Trans. Signal Processing, vol. 50, no. 10, pp. 2599-2613 ,Oct. 2002.
[12] Sheng-Lun Chiou, Kuo-Wang Hsieh, and Ming-Xian Chang, “CSI Reduction of
MIMO-OFDM Systems by Parameterization,” in Proc. IEEE International
Symposium on Personal, Indoor and Mobile Radio Communications, 15-18. Sept.
2008
[13] Ren-Shian Chen and Ming-Xian Chang, “CSI Feedback for closed-loop
MIMO-OFDM systems based on B-splines,” in Proc. IEEE Globe Com Workshop
2010.
[14] M. X. Chang and Y. T. Su, “Model-based channel estimation for OFDM signals in
Rayleigh fading,” IEEE Trans. Commun., vol. 50, pp. 540-544, Apr. 2002.
[15] D.J. Love, R.W. Heath. Jr., W. Santipach, M.L. Honig, “What is the value of limited
feedback for MIMO channels” IEEE Commun. Mag vol. 42, No. 10, pp. 54-59, Oct.
2004.
[16] Tao Cui, “Blind Receiver Design for OFDM Systems Over Doubly Selective
Channels,” IEEE Trans. Commun., vol. 55, no.5, pp. 906 - 917, May 2007.
[17] H. Zhang, Y. Li, V. Stolpman, and N. V. Waes, “A reduced CSI feedback approach
for precoded MIMO-OFDM systems,” IEEE Trans. Wireless Commun., vol. 6, no. 1,
pp. 55-58, Jan. 2007
[18] Channel State Information (CSI) Reduction in Frequency and Time Domain for
Close-loop MIMO-OFDM Systems.