Calibrated Learning for Online Distributed Power Allocation in Small-Cell Networks

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknown a priori, and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies.