  We propose a novel framework for optimizing antenna parameter settings in a
heterogeneous cellular network. We formulate an optimization problem for both
coverage and capacity - in both the downlink (DL) and uplink (UL) - which
configures the tilt angle, vertical half-power beamwidth (HPBW), and horizontal
HPBW of each cell's antenna array across the network. The novel data-driven
framework proposed for this non-convex problem, inspired by Bayesian
optimization (BO) and differential evolution algorithms, is sample-efficient
and converges quickly, while being scalable to large networks. By jointly
optimizing DL and UL performance, we take into account the different signal
power and interference characteristics of these two links, allowing a graceful
trade-off between coverage and capacity in each one. Our experiments on a
state-of-the-art 5G NR cellular system-level simulator developed by AT&T Labs
show that the proposed algorithm consistently and significantly outperforms the
3GPP default settings, random search, and conventional BO. In one realistic
setting, and compared to conventional BO, our approach increases the average
sum-log-rate by over 60% while decreasing the outage probability by over 80%.
Compared to the 3GPP default settings, the gains from our approach are
considerably larger. The results also indicate that the practically important
combination of DL throughput and UL coverage can be greatly improved by joint
UL-DL optimization.
