  The overheads associated with feedback-based channel acquisition can greatly
compromise the achievable rates of FDD based massive MIMO systems. Indeed,
downlink (DL) training and uplink (UL) feedback overheads scale linearly with
the number of base station (BS) antennas, in sharp contrast to TDD-based
massive MIMO, where a single UL pilot trains the whole BS array. In this work,
we propose a graph-theoretic approach to reducing DL training and UL feedback
overheads in FDD massive MIMO systems. In particular, we consider a single-cell
scenario involving a single BS with a massive antenna array serving to
single-antenna mobile stations (MSs) in the DL. We assume the BS employs
two-stage beamforming in the DL, comprising DFT pre-beamforming followed by
MU-MIMO precoding. The proposed graph-theoretic approach exploits knowledge of
the angular spectra of the BS-MS channels to construct DL training protocols
with reduced overheads. Simulation results reveal that the proposed
training-resources allocation method can provide approximately 35% sum-rate
performance gain compared to conventional orthogonal training. Our analysis
also sheds light into the impact of overhead reduction on channel estimation
quality, and, in turn, achievable rates.
