Reinforcement Learning for Uplink Power Control

Abstract: Uplink power control is a resource management functionthat controls the signal’s transmit power from a userdevice, i.e. mobile phone, to a base-station tower. It isused to maximize the data-rates while reducing the generatedinterference.Reinforcement learning is a powerful learning techniquethat has the capability not only to teach an artificial agenthow to act, but also to create the possibility for the agentto learn through its own experiences by interacting with anenvironment.In this thesis we have applied reinforcement learningon uplink power control, enabling an intelligent softwareagent to dynamically adjust the user devices’ transmit powers.The agent learns to find suitable transmit power levelsfor the user devices by choosing a value for the closed-loopcorrection signal in uplink. The purpose is to investigatewhether or not reinforcement learning can improve the uplinkpower control in the new 5G communication system.The problem was formulated as a multi-armed banditat first, and then extended to a contextual bandit. We implementedthree different reinforcement learning algorithmsfor the agent to solve the problem. The performance ofthe agent using each of the three algorithms was evaluatedby comparing the performance of the uplink power controlwith and without the agent. With this approach we coulddiscover whether the agent is improving the performanceor not. From simulations, it was found out that the agentis in fact able to find a value for the correction signal thatimproves the data-rate, or throughput measured in Mbps,of the user devices average connections. However, it wasalso found that the agent does not have a significant contributionregarding the interference.