Millimeter-Wave Base Station Deployment Using the Scenario Sampling Approach

While the Poisson point process (PPP) has been widely employed to model the user distribution in many network design problems, an existing challenge is that it often reveals inaccuracy in small-cell networks. In this paper, instead of employing PPP, we capture the randomness of user equipment (UE) by collecting many their realizations. Specifically, we focus on the millimeter-wave (mmWave) base station (BS) deployment problem in an urban geometry, based on the application of a scenario sampling approach, previously introduced for large-scale optimization, to quantitatively sample a portion of the UE realizations. Motivated by the scenario sampling, a reduced-scale mmWave BS deployment problem is formulated, whose optimal solution is attained by the proposed low-complexity iterative search algorithm. A required number of samples that guarantee a specified majority of the link quality constraints is analyzed. Simulation results verify the scenario sampling theory and the effectiveness of the proposed algorithm.