  Wireless cellular networks have many parameters that are normally tuned upon
deployment and re-tuned as the network changes. Many operational parameters
affect reference signal received power (RSRP), reference signal received
quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and,
ultimately, throughput. In this paper, we develop and compare two approaches
for maximizing coverage and minimizing interference by jointly optimizing the
transmit power and downtilt (elevation tilt) settings across sectors. To
evaluate different parameter configurations offline, we construct a realistic
simulation model that captures geographic correlations. Using this model, we
evaluate two optimization methods: deep deterministic policy gradient (DDPG), a
reinforcement learning (RL) algorithm, and multi-objective Bayesian
optimization (BO). Our simulations show that both approaches significantly
outperform random search and converge to comparable Pareto frontiers, but that
BO converges with two orders of magnitude fewer evaluations than DDPG. Our
results suggest that data-driven techniques can effectively self-optimize
coverage and capacity in cellular networks.
