The significant advancement of the internet-of-things has led to a dramatic increase in the number of moving or motionless devices that are connected to the cellular network. For handling such a great number of devices, 5G networks make use of massive MIMO technology. By considering a dynamic model for all channels variation of devices, joint channel estimation and tracking of all devices are studied regardless of the transmission or non-transmission of pilot by each device in the massive MIMO system. The channel predicting and updating is contingent upon the channel evolution model. This evolution does not necessarily follow a predetermined or fixed model. In this paper, the Recursive Least Squares (RLS) tracker and the Interacting Multiple-Mode (IMM) tracker are developed for tracking these channels. In addition, the autoregressive (AR) coefficients are obtained theoretically for all channels between devices and BS antennas by considering an AR model as an approximation of the channel model between a device and a BS antenna. Consequently, the optimal noise covariance of channel state is obtained adaptively online by means of these coefficients. Furthermore, exponential stability and error bound of the IMM tracker as well as the asymptotic stability of the RLS tracker are derived. Finally, the performance of the introduced trackers is assessed through simulations, and the reduced sum-rate of massive MIMO systems is shown under the effect of time-varying channel.

物联网的显着进步导致连接到蜂窝网络的移动或静止设备的数量急剧增加。为了处理如此大量的设备，5G 网络利用了大规模 MIMO 技术。通过考虑设备所有信道变化的动态模型，研究了大规模多输入多输出系统中，无论每个设备是否传输导频，所有设备的联合信道估计和跟踪。信道预测和更新取决于信道演化模型。这种演变不一定遵循预先确定的或固定的模型。在本文中，递归最小二乘 (RLS) 跟踪器和交互多模式 (IMM) 跟踪器被开发用于跟踪这些通道。此外，通过将 AR 模型视为设备和 BS 天线之间的信道模型的近似，从理论上获得了设备和 BS 天线之间的所有信道的自回归 (AR) 系数。因此，通过这些系数自适应地在线获得信道状态的最优噪声协方差。此外，还推导出了 IMM 跟踪器的指数稳定性和误差界以及 RLS 跟踪器的渐近稳定性。最后，通过仿真评估了引入的跟踪器的性能，并在时变信道的影响下显示了大规模 MIMO 系统的降低总速率。利用这些系数在线自适应地获得信道状态的最优噪声协方差。此外，还推导出了 IMM 跟踪器的指数稳定性和误差界以及 RLS 跟踪器的渐近稳定性。最后，通过仿真评估了引入的跟踪器的性能，并在时变信道的影响下显示了大规模 MIMO 系统的降低总速率。利用这些系数在线自适应地获得信道状态的最优噪声协方差。此外，还推导出了 IMM 跟踪器的指数稳定性和误差界以及 RLS 跟踪器的渐近稳定性。最后，通过仿真评估了引入的跟踪器的性能，并在时变信道的影响下显示了大规模 MIMO 系统的降低总速率。