Small Cell Transmit Power Assignment Based on 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. 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 with 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.