Uplink-Downlink Joint Antenna Optimization in Cellular Systems with Sample-Efficient Learning.

In this paper, we jointly optimize the capacity and coverage of both uplink and downlink transmissions by tuning the downtilt angle, vertical half-power beamwidth (HPBW), and horizontal HPBW of each cell's antenna array across a heterogeneous cellular network. We formulate an optimization problem and propose a novel sample-efficient algorithm to solve this non-convex problem. We evaluate our framework on a state-of-theart cellular system-level simulator developed by AT&T Labs by comparing it with the 3GPP baseline. Example results tuned to optimize uplink coverage and downlink rate indicate that jointly optimizing the uplink and downlink directions improves uplink median and 5% outage SINR by (i) 1.6 dB and 4.5 dB, respectively, compared to downlink only-optimization and by (ii) 6.7 dB and 14.6 dB compared to the 3GPP baseline. Simultaneously, we can increase downlink median and outage SINR by comparable amounts compared to uplink-only optimization, but with larger gains in median SINR and downlink sum-rate. Our results indicate that there are significant gains to be harvested from site-specific data-driven base station parameter optimization, and they can be achieved in a scalable and automated fashion.