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.

本文提出了一种基于随机赌博理论和贝叶斯原理的小型基站传输功率分配算法，其通过利用人类用户行为的简单覆盖反馈，极大地减少了计划人员的人力测量。该算法在考虑了功率切换惩罚的同时，将相似功率值之间的性能相关性纳入考虑，利用先前的先验知识恢复了更多的性能下降，并达到了最优长期功率设置。