  Massive multi-input multi-output (Massive MIMO) has been recognized as a key
technology to meet the demand for higher data capacity and massive
connectivity. Nevertheless, the number of active users is restricted due to
training overhead and the limited coherence time. Current wireless systems
assume the same coherence slot duration for all users, regardless of their
heterogeneous Doppler spreads. In this paper, we exploit this neglected degree
of freedom in addressing the training overhead bottleneck. We propose a new
uplink training scheme where the periodicity of pilot transmission differs
among users based on their actual channel coherence times. Since the changes in
the wireless channel are, primarily, due to movement, uplink training decisions
are optimized, over long time periods, while considering the evolution of the
users channels and locations. Owing to the different rates of the wireless
channel and location evolution, a two time scale control problem is formulated.
In the fast time scale, an optimal training policy is derived by choosing which
users are requested to send their pilots. In the slow time scale, location
estimation decisions are optimized. Simulation results show that the derived
training policies provide a considerable improvement of the cumulative average
spectral efficiency even with partial location knowledge.
