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We propose a novel framework for optimizing antenna parameter settings in aheterogeneous cellular network. We formulate an optimization problem for bothcoverage and capacity - in both the downlink (DL) and uplink (UL) - whichconfigures the tilt angle, vertical half-power beamwidth (HPBW), and horizontalHPBW of each cell's antenna array across the network. The novel data-drivenframework proposed for this non-convex problem, inspired by Bayesianoptimization (BO) and differential evolution algorithms, is sample-efficientand converges quickly, while being scalable to large networks. By jointlyoptimizing DL and UL performance, we take into account the different signalpower and interference characteristics of these two links, allowing a gracefultrade-off between coverage and capacity in each one. Our experiments on astate-of-the-art 5G NR cellular system-level simulator developed by AT&T Labsshow that the proposed algorithm consistently and significantly outperforms the3GPP default settings, random search, and conventional BO. In one realisticsetting, and compared to conventional BO, our approach increases the averagesum-log-rate by over 60% while decreasing the outage probability by over 80%.Compared to the 3GPP default settings, the gains from our approach areconsiderably larger. The results also indicate that the practically importantcombination of DL throughput and UL coverage can be greatly improved by jointUL-DL optimization.

Joint Uplink-Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings