Sample-Efficient Learning of Cellular Antenna Parameter Settings

Finding an optimum configuration of base station (BS) antenna parameters is a challenging, non-convex problem for cellular networks. The chosen configuration has major implications for coverage and throughput in real-world systems, as it effects signal strength differently throughout the cell, as well as dictating the interference caused to other cells. In this paper, we propose a novel and sample-efficient data-driven methodology for optimizing antenna downtilt angles. Our approach combines Bayesian Optimization (BO) with Differential Evolution (DE): BO decreases the computational burden of DE, while DE helps BO avoid the curse of dimensionality. We evaluate the performance on a realistic state-of-the-art cellular system simulator developed by AT&T Labs, that includes all layers of the protocol stack and sophisticated channel models. Our results show that the proposed algorithm outperforms Bayesian optimization, random selection, and the baseline settings adopted in 3GPP by nontrivial amounts in terms of both capacity and coverage. Also, our approach is notably more time-efficient than DE alone.