双选择信道下叠加训练OFDM系统的迭代信道估计与均衡

在频率和时间双选择性信道下，时域冲击响应将会在一个OFDM符号时间内发生变化，导致OFDM系统出现严重的子载波间干扰。本文提出一种在双选择性信道下利用叠加训练序列进行迭代信道估计和均衡的方案。在假设时域信道抽头系数服从指数基扩展参数模型以后，利用在一个符号内的已知叠加周期训练序列建立线性模型，并基于最小二乘准则求出信道参数模型的各个抽头参数，从而构造出对应的时域信道响应矩阵和频域均衡矩阵。为进一步改善估计和均衡性能，将所得的符号判决作为已知的训练序列进行迭代估计和均衡，从而能有效地增强信道估计的性能并消除ICI干扰。实验结果表明本文算法在时间－频率双选择性信道下具有较好的性能。

Iterative Channel Estimation and Equalization for Superimposed Training OFDM Systems over Doubly Selective Channels

In time-frequency selective wireless channel, channel impulse response is changing in one OFDM data block and cause inter-carrier interference. A novel time-domain channel estimation scheme is proposed using superimposed training for OFDM systems in the above environment. Based on the basis expansion model, a linear model is established using the periodic superimposed training, and the least square estimate of this model parameter is obtained. This estimate is used to construct the frequency domain equalization matrix further. In order to improve the performance, the channel estimate uses the symbol decision as the new training sequence. And the iterative channel estimation and equalization is constructed. Simulation result shows the effectiveness of our proposed scheme in doubly selective channel.