  In this paper, adaptive training beam sequence design for efficient channel
estimation in large millimeter-wave(mmWave) multiple-input multiple-output
(MIMO) channels is considered. By exploiting the sparsity in large mmWave MIMO
channels and imposing a Markovian random walk assumption on the movement of the
receiver and reflection clusters, the adaptive training beam sequence design
and channel estimation problem is formulated as a partially observableMarkov
decision process (POMDP) problem that finds non-zero bins in a two-dimensional
grid. Under the proposed POMDP framework, optimal and suboptimal adaptive
training beam sequence design policies are derived. Furthermore, a very fast
suboptimal greedy algorithm is developed based on a newly proposed reduced
sufficient statistic to make the computational complexity of the proposed
algorithm low to a level for practical implementation. Numerical results are
provided to evaluate the performance of the proposed training beam design
method. Numerical results show that the proposed training beam sequence design
algorithms yield good performance.
