基于叠加训练序列的低复杂度时变信道估计方案
doi: 10.3724/SP.J.1146.2012.01742
A Low Complexity Time-varying Channel Estimation Scheme Based on Superimposed Trainin
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摘要: 与传统的时分/频分复用训练序列相比,采用叠加训练序列的传输方案可以有效地提高系统的频谱利用率。然而,叠加方案中训练序列与信息序列的相互干扰会造成系统性能的严重下降,如何有效消除信息干扰是提高信道估计性能的关键。该文针对时变衰落信道,首先提出一种新的基于一阶统计量信道估计算法。该算法利用基扩展模型(BEM)构建时变信道,通过时域分块平均的方法来抑制信息序列干扰。在此基础上,利用信息序列和训练序列经历相同信道衰落的特性,提出一种基于加权最小二乘(WLS)的迭代信道估计与检测方案。新方案利用Kalman滤波检测器代替确定性最大似然(DML)检测器,将检测符号序列看作附加的训练序列用于信道估计,从而可以显著提高信道估计性能。仿真结果表明,新方案可以有效消除信息序列干扰,且性能和计算复杂度均优于现有的同类方案。Abstract: Compared to traditional Time Division Multiplexed (TDM) and Frequency Division Multiplexed (FDM) training sequence, Superimposed Training (ST) sequence can effectively improve the frequency spectrum efficiency. However, the interference between information and training sequences in ST causes severe degradation on the performance of the system. The crux of improving channel estimation performance is to cancel the information sequence interference effectively. This paper firstly proposes a new first-order statistic-based channel estimation algorithm for the time-varying channel. In this algorithm, the time-varying channel is approximated by the basis expansion model. The information sequence interference is suppressed by calculating mean of the partitioned sequence in time domain. On this basis, an iterative channel estimation and detection scheme is proposed according to that the information and training sequences undergo the identical fading channel. In the new scheme, the Deterministic Maximum Likelihood (DML) detector is substituted by a Kalman filtering detector. The detected symbols are seemed as additional training sequence, which increasing the channel estimation performance remarkably. The simulation results show that the new scheme not only cancels the information sequence interference effectively, but also include better performance and lower computation complexity compared to other schemes.
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