论文标题
随着时间变化的通道，多细胞大型MIMO系统的半盲通道估计和数据检测
Semi-blind Channel Estimation and Data Detection for Multi-cell Massive MIMO Systems on Time-Varying Channels
论文作者
论文摘要
我们研究了具有空间相关的时变通道的多细胞大型MIMO系统上行链路中半盲通道估计和符号检测的问题。基于期望传播（EP）的算法是为了迭代近似于未知通道矩阵的后验分布和带有指数族分布的传输数据符号的关节A后验分布。然后，该分布用于直接估计通道矩阵和数据符号的检测。流行的Kalman过滤算法的修改版本称为KF-M，来自我们的EP推导，它用于初始化基于EP的算法。还检查了Kalman平滑算法的性能，其次是KF-M。仿真结果表明，随着基于基站天线的数量的增加以及传输框架的长度，半盲KF-M，KS-M和基于EP的算法的通道估计误差和符号误差率（SER）改进。结果表明，基于EP的算法在通道估计和符号检测中显着优于KF-M和KS-M算法。最后，我们的结果表明，当应用于随时间变化的通道时，这些算法的表现优于为块降压通道模型开发的算法。
We study the problem of semi-blind channel estimation and symbol detection in the uplink of multi-cell massive MIMO systems with spatially correlated time-varying channels. An algorithm based on expectation propagation (EP) is developed to iteratively approximate the joint a posteriori distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. This distribution is then used for direct estimation of the channel matrix and detection of data symbols. A modified version of the popular Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize the EP-based algorithm. Performance of the Kalman smoothing algorithm followed by KF-M is also examined. Simulation results demonstrate that channel estimation error and the symbol error rate (SER) of the semi-blind KF-M, KS-M, and EP-based algorithms improve with the increase in the number of base station antennas and the length of the transmitted frame. It is shown that the EP-based algorithm significantly outperforms KF-M and KS-M algorithms in channel estimation and symbol detection. Finally, our results show that when applied to time-varying channels, these algorithms outperform the algorithms that are developed for block-fading channel models.
