  We propose a hierarchical interference mitigation scheme for massive MIMO
cellular networks. The MIMO precoder at each base station (BS) is partitioned
into an inner precoder and an outer precoder. The inner precoder controls the
intra-cell interference and is adaptive to local channel state information
(CSI) at each BS (CSIT). The outer precoder controls the inter-cell
interference and is adaptive to channel statistics. Such hierarchical precoding
structure reduces the number of pilot symbols required for CSI estimation in
massive MIMO downlink and is robust to the backhaul latency. We study joint
optimization of the outer precoders, the user selection, and the power
allocation to maximize a general concave utility which has no closed-form
expression. We first apply random matrix theory to obtain an approximated
problem with closed-form objective. We show that the solution of the
approximated problem is asymptotically optimal with respect to the original
problem as the number of antennas per BS grows large. Then using the hidden
convexity of the problem, we propose an iterative algorithm to find the optimal
solution for the approximated problem. We also obtain a low complexity
algorithm with provable convergence. Simulations show that the proposed design
has significant gain over various state-of-the-art baselines.
