Uplink Power Control Framework Based on Reinforcement Learning for 5G Networks

Abstract

In this work, we propose an uplink power control (PC) framework compliant with the technical specifications of the fifth generation (5G) wireless networks. We apply the fundamentals of reinforcement learning (RL) to develop a power control algorithm able to learn a strategy that enhances the total data rate on the uplink channel and mitigates the neighbor cell interference. The base station (BS) uses a set of commands to specify by how much the user equipment (UE) transmit power should change. After implementing such commands, each UE reports a set of information to its serving BS, and this, in turn, predicts the next commands to achieve a suitable UE transmit power level. The BS converts the UE reports into rewards according to a predefined cost function, which impacts the longterm behavior of the UE transmit power. The simulation results indicate a near-optimum performance of the proposed framework in terms of total transmit power, total data rate, and network energy efficiency. It provides a self-exploratory power control strategy that overcomes soft dropping power control (SDPC) with similar signaling levels.