Adaptive Safety Shields for Reinforcement Learning-Based Cell Shaping

Abstract

Adjusting the remote electrical tilt (RET) of antennas is one of the important actions targeting run-time optimization of key performance indicators (KPIs) related to service quality in wireless self-organizing networks (SONs). Reinforcement learning (RL) is one of the preferred Machine Learning methods for automating the choice of RET for all the antennas managed by a company in a region. The automated system should ensure that the system will operate within a safe region to maintain a minimum defined service quality. The safe region of operation is typically customizable based on the targeted service quality at any point in time. This customizable nature of the safe region necessitates automated learning of adaptive safety shields for steering the RL agent away from unsafe regions. This paper presents an adaptive safety shield framework that is capable of learning such shields during the training phase of the RL agent. Our adaptive safety shield framework has been evaluated in different RET scenarios, and we have shown the benefits of our proposed framework over the Baseline method currently in use and a vanilla RL-based method in terms of both safety and performance metrics.