Small Cell Power Assignment with Unimodal Continuum-Armed Bandit Learning

Judiciously setting the base station transmit power that matches its deployment environment is a key problem in ultra dense networks and heterogeneous in-building cellular deployments. A unique characteristic of this problem is the tradeoff between sufficient indoor coverage and limited outdoor leakage, which has to be met without explicit knowledge of the environment. In this paper, we address the small base station(SBS) transmit power assignment problem based on stochastic bandit learning with a continuous set of arms to avoid the constant performance loss or heavy workload on initialization caused by crude or excessive sampling in the previous strategies. With the aim of minimizing the expected cumulative performance loss, we capture the unimodality of the performance function which efficiently accelerates the search for the globally optimal power value. Simulations mimicking practical deployments are performed for both single and multiple SBS scenarios, and the resulting power settings are compared to the state-of-the-art solutions. Significant performance gains of the proposed algorithms are observed.