  Millimeter wave provides a very promising approach for meeting the
ever-growing traffic demand in next generation wireless networks. To utilize
this band, it is crucial to obtain the channel state information in order to
perform beamforming and combining to compensate for severe path loss. In
contrast to lower frequencies, a typical millimeter wave channel consists of a
few dominant paths. Thus it is generally sufficient to estimate the path gains,
angles of departure (AoDs), and angles of arrival (AoAs) of those paths.
Proposed in this paper is a dual timescale model to characterize abrupt channel
changes (e.g., blockage) and slow variations of AoDs and AoAs. This work
focuses on tracking the slow variations and detecting abrupt changes. A Kalman
filter based tracking algorithm and an abrupt change detection method are
proposed. The tracking algorithm is compared with the adaptive algorithm due to
Alkhateeb, Ayach, Leus and Heath (2014) in the case with single radio frequency
chain. Simulation results show that to achieve the same tracking performance,
the proposed algorithm requires much lower signal-to-noise-ratio (SNR) and much
fewer pilots than the other algorithm. Moreover, the change detection method
can always detect abrupt changes with moderate number of pilots and SNR.
