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首页> 外文期刊>Signal Processing, IEEE Transactions on >Nondata-Aided Joint Channel Estimation and Equalization for OFDM Systems in Very Rapidly Varying Mobile Channels
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Nondata-Aided Joint Channel Estimation and Equalization for OFDM Systems in Very Rapidly Varying Mobile Channels

机译：瞬息万变的移动信道中OFDM系统的非数据辅助联合信道估计和均衡

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摘要

This paper is concerned with the challenging and timely problem of joint channel estimation and equalization for orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency selective and very rapidly time varying channels. The resulting algorithm is based on the space alternating generalized expectation maximization—maximum a posteriori probability (SAGE-MAP) technique which is particularly well suited to multicarrier signal formats. The algorithm is implemented in the time-domain which enables one to use the Gaussian approximation of the transmitted OFDM samples. Consequently, the averaging process of the nonpilot data symbols becomes analytically possible resulting in a feasible and computationally efficient channel estimation algorithm leading to a receiver structure that yields also an equalized output from which the data symbols are detected with excellent symbol error rate (SER) performance. Based on this Gaussian approximation the exact Bayesian Cramér Rao lower bound (CRLB) as well as the convergence rate of the algorithm are derived analytically. To reduce the computational complexity of the algorithm, discrete Legendre orthogonal basis functions are employed to represent the rapidly time-varying fading channel. It is shown that, depending on the normalized Doppler frequency, only a small number of expansion coefficients is sufficient to approximate the channel very well and there is no need to know the correlation function of the input signal. The computational complexity of the algorithm is shown to be ${sim}O(NL)$ per detected data symbol and per SAGE-MAP algorithm cycle where $N$ is the number of OFDM subcarriers and $L$ is the number of multipath components.
机译：本文关注的是在存在选频和时变非常快的信道的情况下，正交频分复用（OFDM）系统的联合信道估计和均衡的挑战性和及时性问题。生成的算法基于空间交替广义期望最大化-最大后验概率（SAGE-MAP）技术，该技术特别适合于多载波信号格式。该算法在时域中实现，这使人们能够使用所传输OFDM样本的高斯近似。因此，非导频数据符号的平均过程在分析上变得可能，从而导致可行且计算效率高的信道估计算法，从而导致接收器结构也产生均衡的输出，从该输出中可以检测到具有出色符号错误率（SER）性能的数据符号。基于此高斯近似，可以解析得出精确的贝叶斯CramérRao下界（CRLB）以及算法的收敛速度。为了降低算法的计算复杂度，采用离散的勒让德正交基函数来表示快速时变衰落信道。结果表明，取决于归一化的多普勒频率，仅少量的扩展系数就足以很好地逼近信道，并且无需知道输入信号的相关函数。该算法的计算复杂度显示为每个检测到的数据符号和每个SAGE-MAP算法周期的$ {sim} O（NL）$，其中$ N $是OFDM子载波的数量，$ L $是多径分量的数量。

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