Learn to adapt: Self-optimizing small cell transmit power with correlated 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 theory. We explicitly consider power switching penalties to discourage frequent changes of the transmit power, which causes varying coverage and uneven user experience. Unlike existing solutions that rely on RF surveys in the target area, we take advantage of the user behavior with simple coverage feedback in the network. In addition, the proposed power assignment algorithms follow the Bayesian principle to utilize the available prior knowledge and correlation structure from the self configuration phase. 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.

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