Belief-aided Robust Control for Remote Electrical Tilt Optimization

Abstract: Remote Electrical Tilt (RET) is a method for configuring antenna downtilt in base stations to optimize mobile network performance. Reinforcement Learning (RL) is an approach to automating the process by letting an agent learn an optimal control strategy and adapt to the dynamic environment. Applying RL in real world comes with challenges, for the RET problem there are performance requirements and partial observability of the system through exogenous factors inducing noise in observations. This thesis proposes a solution method through modeling the problem by a Partially Observable Markov Decision Process (POMDP). The set of hidden states are modeled as a high- level representation of situations requiring one of the possible actions uptilt, downtilt, no change. From this model, a Bayesian Neural Network (BNN) is trained to predict an observation model, relating observed Key Performance Indicators (KPIs) to the hidden states. The observation model is used for estimating belief state probabilities of each hidden state, from which decision of control action is made through a restrictive threshold policy. Experiments comparing the method to a baseline Deep Q- network (DQN) agent shows the method able to reach the same average performance increase as the baseline while outperforming the baseline in two metrics important for robust and safe control behaviour, the worst- case minimum reward increase and the average reward increase per number of tilt actions.