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.

中文翻译：

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时变信道上多小区大规模 MIMO 系统的半盲信道估计和数据检测

我们研究了具有空间相关时变信道的多小区大规模 MIMO 系统上行链路中的半盲信道估计和符号检测问题。开发了一种基于期望传播 (EP) 的算法，以迭代地近似未知信道矩阵的联合后验分布和具有指数族分布的传输数据符号。然后将该分布用于信道矩阵的直接估计和数据符号的检测。我们的 EP 推导中出现了流行的 Kalman 滤波算法的修改版本，称为 KF-M，它用于初始化基于 EP 的算法。还检查了 KF-M 之后的卡尔曼平滑算法的性能。仿真结果表明，基于半盲KF-M、KS-M和EP算法的信道估计误差和误码率（SER）随着基站天线数量的增加和传输长度的增加而改善。框架。结果表明，基于EP的算法在信道估计和符号检测方面明显优于KF-M和KS-M算法。最后，我们的结果表明，当应用于时变信道时，这些算法优于为块衰落信道模型开发的算法。