外文翻译--基于线性预测方法的水声信道最小均方误差盲均衡（节选）

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1、 中文 3775 字 毕业设计 (论文 )外文资料翻译 系别 电子信息系 专业 通信工程 班级 姓名 学号 外文出处 J. Marine Sci. Appl. (2011) 10: 113-120 附件 1. 原文； 2. 译文 2013 年 03 月 1 Blind Adaptive MMSE Equalization of Underwater Acoustic Channels Based on the Linear Prediction Method R Bragos, R Blanco-Enrich Abstract: The problem of blind adaptive eq

2、ualization of underwater single-input multiple-output (SIMO)acoustic channels was analyzed by using the linear prediction method. Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification. Two methods forcalculating linear MMSE equaliz

3、ers were proposed. One was based on full channel identification and realizedusing RLS adaptive algorithms, and the other was based on the zero-delay MMSE equalizer and realized usingLMS and RLS adaptive algorithms, respectively. Performance of the three proposed algorithms and comparison with two ex

4、isting zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels. The results show that the proposed algorithms are robust enough to channel order mismatch. They have almost the same performance as the corresponding ZF algorithms under a hi

5、gh signal-to-noise (SNR) ratio and better performance under a low SNR. 1 Introduction Time-varying characteristic and multi-path fading of underwater acoustic channels can induce severe inter symbol interference (ISI) in high data rate communication systems. Channel equalization applying adaptive fi

6、lters is one of the techniques to mitigate the effects of ISI. Conventionally, the initialization of an adaptive filter is achieved by a known training sequence from a transmitter before data transmission, so that valuable channel capacity is reduced. Recently, blind equalization technique (Stojanov

7、ic, 1996) has attracted more and more attention. Compared with adaptive equalization technique, the major advantage of such technique is that no training sequence is needed to start up or restart the system whenever the communication breaks down unpredictably. Traditionally, symbol rate sampled chan

8、nel output sequence is stationary and higher order statistics are used to estimate the channel and to calculate the equalizer. More recently, it has been shown that the channel output sequence is cyclostationary if the sampling rate exceeds the symbol rate, and then second-order statistics (SOS) 2 c

9、ontain sufficient information to estimate most communication channels using cyclostationarity (Tong et al., 1994； Tong et al., 1995； Papadias and Slock, 1999). Based on the seminar work of Tong et al. (1994), many effective blind methods have been proposed for estimating the channel from the output

10、of only second-order statistics. However, it turns out that these methods have much computational complexity or they are very sensitive to channel order mismatch (Moulines et al., 1995； Meraim et al., 1997； Liu et al., 1994； Alberge et al., 2002), which are major obstacles for their real-time implem

11、entations. The prediction error method offers an alternative to the class of techniques above. It was introduced by Slock (1994), Meraim et al. (1997), Ding (1997), Gesber and Duhamei (1997), Tugnait (1999) and offered great advantages over other SOS based techniques because of its robustness to cha

12、nnel order mismatch and low computational complexity. Based on multichannel linear prediction of the observations, zero-forcing (ZF) and minimum mean square error (MMSE) equalizers with arbitrary delay were investigated in Papadias and Slock (1999). Nevertheless, when calculating ZF equalizers, not

13、only the (n+1)-step-ahead linear prediction of the noise-free channel output should be estimated, but also the backward linear prediction of some sufficient order M of the prediction error of the previous (n+1)-step-ahead linear predictor need to be carried out. When calculating MMSE equalizers, ZF

14、equalizers should be worked out first and noise variance must be estimated correctly. These operations make the two kinds of equalizers very complicated and hard to realize. A computationally effective blind ZF equalization method has been discussed in Li and Fan (2000). It is based on two linear pr

15、ediction models, one is used to calculate the zero-delay ZF equalizer and the other is used to calculate ZF equalizers with arbitrary delay on the basis of the first one. However, only ZF equalizers are presented. In Giannaki andHalford (1997), an approach for directly estimating nonzerodelay MMSE e

16、qualizers was given. Nevertheless, the first coefficient of the channel response must be known a priori and noise variance should be estimated correctly. In order to improve the performance of blind equalizers without the aforementioned limitations, two methods for finding linear MMSE equalizers with arbitrary delay are presented in this paper. One is based on full channel identification and realized using RLS adaptive algorithm, the other is based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms, respectively.