  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. Unlike existing solutions that rely
on heavy involvement of RF engineers surveying the target area, we take
advantage of the human user behavior with simple coverage feedback in the
network, and thus significantly reduce the planned human measurement. In
addition, the proposed power assignment algorithms follow the Bayesian
principle to utilize the available prior knowledge from system self
configuration. To guarantee good performance when the prior knowledge is
insufficient, we incorporate the performance correlation among similar power
values, and establish an algorithm that exploits the correlation structure to
recover majority of the degraded performance. Furthermore, we explicitly
consider power switching penalties in order to discourage frequent changes of
the transmit power, which cause varying coverage and uneven user experience.
Comprehensive system-level simulations are performed for both single and
multiple SBS deployment scenarios, and the resulting power settings are
compared to the state-of-the-art solutions. Significant performance gains of
the proposed algorithms are observed. Particularly, the correlation structure
enables the algorithm to converge much faster to the optimal long-term power
than other methods.
