  Optimizing the sum-log-utility for the downlink of multi-frequency band,
multiuser, multiantenna networks requires joint solutions to the associated
beamforming and user scheduling problems through the use of cloud radio access
network (CRAN) architecture; optimizing such a network is, however, non-convex
and NP-hard. In this paper, we present a novel iterative beamforming and
scheduling strategy based on fractional programming and the Hungarian
algorithm. The beamforming strategy allows us to iteratively maximize the
chosen objective function in a fashion similar to block coordinate ascent.
Furthermore, based on the crucial insight that, in the downlink, the
interference pattern remains fixed for a given set of beamforming weights, we
use the Hungarian algorithm as an efficient approach to optimally schedule
users for the given set of beamforming weights. Specifically, this approach
allows us to select the best subset of users (amongst the larger set of all
available users). Our simulation results show that, in terms of average
sum-log-utility, as well as sum-rate, the proposed scheme substantially
outperforms both the state-of-the-art multicell weighted minimum mean-squared
error (WMMSE) and greedy proportionally fair WMMSE schemes, as well as standard
interior-point and sequential quadratic solvers. Importantly, our proposed
scheme is also far more computationally efficient than the multicell WMMSE
scheme.
