Safe Reinforcement Learning for Remote Electrical Tilt Optimization

Sammanfattning: The adjustment of the vertical tilt angle of Base Station (BS) antennas, also known as Remote Electrical Tilt (RET) optimization, is a simple and efficient method of optimizing modern telecommunications networks. Reinforcement Learning (RL) is a machine learning framework that can solve complex problems like RET optimization due to its capability to learn from experience and adapt to dynamic environments. However, conventional RL methods involve trial-and-error processes which can result in short periods of poor network performance which is unacceptable to mobile network operators. This unreliability has prevented RL solutions from being deployed in real-world mobile networks. In this thesis, we formulate the RET optimization problem as a Safe Reinforcement Learning (SRL) problem and attempt to train an RL policy that can offer performance improvement guarantees with respect to an existing baseline policy. We utilize a recent SRL method called Safe Policy Improvement through Baseline Bootstrapping (SPIBB) to improve over a baseline by training an RL agent on a offline dataset of environment interactions gathered by the baseline. We evaluate our solution using a simulated environment and show that it is effective at improving a tilt update policy in a safe manner, thus providing a more reliable RL solution to the RET optimization problem and potentially enabling future real-world deployment.