Millimeter Wave Massive MIMO Channel Estimation and Tracking

With the rapid development of 5G, massive MIMO technology has become one of the most important technologies in 5G. However, while massive MIMO technology could provide reliable performance guarantee, with the number of antennas increases, the problem of channel estimation has also become more complicated. To solve this problem, we propose a channel state estimation and tracking algorithm based on particle filter, when considering the temporal correlation between the millimeter wave narrowband block fading channels. A channel state model including channel gain, the angle of arrival, and the angle of departure has been established. A performance comparison is carried out, in terms of normalized mean square error, considering massive MIMO channel estimation, for different algorithms. We also take into account the performance affected by signal to noise ratio and the number of antennas. Numerical results show that the performance can be considerably improved in the case of a large number of antennas over the conventional scheme. Furthermore, this algorithm also has better performance under traditional MIMO conditions.