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標題: 正交分頻多工系統下使用ρ-LMS演算法的適應性通道預測
Adaptive channel prediction in OFDM systems using the ρ-LMS algorithm
作者: 王勛弘
Wang, shiun Hong
關鍵字: OFDM;正交分頻多工;channel prediction;adaptive algorithm;通道預測;適應性演算法
出版社: 電機工程學系所
引用: [1] Schafhuber, D., Matz, G., “MMSE and Adaptive Prediction of Time-Varying Channels for OFDM Systems,” IEEE Trans. Commun., Vol. 4, No. 2, pp. 593-602, Mar. 2005, [2] W. R. Wu and P. C. Chen, “Adaptive AR Modeling in White Gaussian Noise,” IEEE Trans. Signal. Process., Vol. 45, NO. 5, pp. 1184-1192, MAY 1997 [3] R. W. Chang, “Synthesis of band-limited orthogonal signals for multi-channel data transmission,” Bell Syst. Tech. J., vol. 45, pp. 1775-1796, Dec. 1996. [4] A. Peled and A. Ruiz, “Frequency domain data transmission using reduced computational complexity algorithm,” in Proc. IEEE ICASSP-80, Denver, CO, 1980, pp. 964-967. [5] L. J. Cimini, “Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing,” IEEE Trans. Commun., vol.33, no. 7, pp. 665-675, Jul. 1985. [6] J. A. C. Bingham, “Multicarrier modulation for data transmission: An idea whose time has come,” IEEE Commun. Mag., col. 28, pp. 5-14, May 1990. [7] A.J. Goldsmith and S. G. Chua, “Adaptive Coded Modulation for Fading Channels,” IEEE Trans. Commun, vol. 46, pp. 595-601, May 1998. [8] A. J. Goldsmith and S. G. Chua, “Variable-Rate Variable-Power MQAM for Fading Channels,” IEEE Trans. Common., vol. 45, pp. 1218-1230, Oct. 1997 [9] Thomas Keller and Lajos Hanzo, “Adaptive Modulation Techniques for Duplex OFDM Transmission,” IEEE Trans. Technol., vol. 49, No. 5, Sept. 2000. [10] A. J. Goldsmith, “Adaptive Modulation and Coding for Fading Channels,” Information Theory and Communication Workshop, 1999. Proceedings of the 1999 IEEE , June 1999, pp.24-26 [11] B. Vucetic, “An adaptive coding scheme for time-varying channels,” IEEE Trans. Commun., Vol. 39, No. 5, pp. 653-663, May 1991. [12] G. Caire, G. Taricco, and E. Biglieri, “Optimum power control over fading channels,” IEEE Trans. Inform. Theory, Vol. 45, No. 5, pp.1468-1489, July 1999. [13] M. Raitola, A. Hottinen, and R. Wichman, “Transmission Diversity in Wideband CDMA,” IEEE Veh. Technol. Conf., Vol. 2, pp. 1545-1549, May 1999.
適應性通道預測,利用過去的通道估計值,產生未來的通道係數預測值,有助於減少估計通道狀態時所需的訓練資料(training data)數目,克服通道估計過載(overhead)的問題。在[1]中使用常見的NLMS演算法,從包含雜訊的LS通道估計結果,來更新通道預測器的係數。由於LS通道估計誤差影響了預測器係數的調整,將進而降低預測值的準確性。為了改善NLMS適應性演算法的性能,本論文提出使用ρ-LMS演算法來更新通道預測器的係數。
在ρ-LMS演算法中,針對多路徑傳輸通道中的每個路徑之振幅係數,其時變特性,以一個AR process來表示,且藉由一個近似條件平均(conditional mean)的估計器來產生估計值,接著再使用估計出的通道係數,作為標準LMS演算法的輸入資料來更新預測器的係數。因此,預測通道雜訊對於預測準確度的影響就可以被減緩。我們也將比較在時域和頻域上預測的效能。

OFDM is an efficient modulation scheme for broadband wireless communications. Most OFDM systems use coherent detection, which has approximately 3dB SNR gain over differential detection. To perform coherent detection, the channel state information is needed at the receiver. Furthermore, in order to enable adaptive modulation and coding at the transmitter to improve the system capacity and link reliability, the channel state information is also required at the transmitter.
Adaptive channel prediction can help reduce the channel estimation overhead in OFDM systems by reducing the number of training data for estimating the channel
state. The channel prediction scheme proposed in [1] uses the conventional LMS algorithm to update the channel predictor coefficient from the noisy least-square
channel estimate. The prediction accuracy of this algorithm will be degraded by the LS estimation error resulting from the channel noise. To improve the performance of the LMS adaptive algorithm, in this thesis we propose to use the ρ-LMS algorithm to update the channel predictor coefficient.
In the ρ-LMS algorithm , each fading channel coefficient is modeled as an AR process and estimated by an approximated conditional mean estimator, then the
standard LMS algorithm is performed to update the predictor coefficient using the estimated channel coefficient. As a result, the channel noise effect on the prediction accuracy can be alleviated. We will also compare the performance of time-domain prediction with that of frequency-domain prediction.
其他識別: U0005-2007200713273400
Appears in Collections:電機工程學系所

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