It is well-known that time-varying channels can provide time diversity and improve error rate performance compared to time-invariant fading channels. However, exploiting time diversity requires very accurate channel estimates at the receiver. In order to reduce the number of unknown channel coefficients while estimating the time-varying channel, basis expansion models can be used along with long transmission frames that contain multiple orthogonal frequency division multiplexing (OFDM) symbols that experience the channel variation. The design of these OFDM frames need to judiciously incorporate training and data insertions in the transmitted signal while maintaining orthogonality. In this work, we propose an inter channel interference (ICI)-free training model depending on pilot symbols only and provide a corresponding time-varying channel estimation method. This scheme relies on an algorithm to determine the number of OFDM symbols per frame and the number of basis functions per path with minimal information about the Doppler bandwidth. As a performance benchmark, Bayesian Cramér Rao lower bound (CRLB) and the corresponding MSE bound are derived analytically for the proposed training model. Theoretical MSE expressions of the proposed estimation scheme are also derived as well as the MSE expressions in the presence of Doppler frequency mismatch. Simulations exhibit substantial MSE improvement and the corresponding Symbol Error Rate (SER) performances of the low complexity estimation scheme. They also corroborate that, unlike the common results in the literature, an OFDM system can perform better as the Doppler frequency increases with judicious design of training and channel estimation schemes.

中文翻译：

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OFDM 系统快速变化移动信道的基于子空间的估计

众所周知，与时不变衰落信道相比，时变信道可以提供时间分集并提高误码率性能。然而，利用时间分集需要在接收器非常准确的信道估计。为了在估计时变信道的同时减少未知信道系数的数量，可以将基扩展模型与包含经历信道变化的多个正交频分复用 (OFDM) 符号的长传输帧一起使用。这些 OFDM 帧的设计需要在传输信号中明智地结合训练和数据插入，同时保持正交性。在这项工作中，我们提出了一种仅依赖导频符号的无信道间干扰 (ICI) 训练模型，并提供了相应的时变信道估计方法。该方案依赖于一种算法来确定每帧 OFDM 符号的数量和每条路径的基函数数量，以及关于多普勒带宽的最少信息。作为性能基准，贝叶斯 Cramér Rao 下限 (CRLB) 和相应的 MSE 界限是针对所提出的训练模型分析得出的。还导出了所提出的估计方案的理论 MSE 表达式以及多普勒频率失配情况下的 MSE 表达式。仿真展示了低复杂度估计方案的显着 MSE 改进和相应的符号错误率 (SER) 性能。他们也证实，