A Graph Attention Learning Approach to Antenna Tilt Optimization
This addresses the problem of suboptimal performance and scalability in mobile network optimization for telecom operators, though it is incremental as it builds on existing RL methods.
The paper tackles antenna tilt optimization in 6G networks by proposing a Graph Attention Q-learning (GAQ) algorithm, which outperforms standard DQN methods by a large margin and generalizes to various network sizes and densities.
6G will move mobile networks towards increasing levels of complexity. To deal with this complexity, optimization of network parameters is key to ensure high performance and timely adaptivity to dynamic network environments. The optimization of the antenna tilt provides a practical and cost-efficient method to improve coverage and capacity in the network. Previous methods based on Reinforcement Learning (RL) have shown great promise for tilt optimization by learning adaptive policies outperforming traditional tilt optimization methods. However, most existing RL methods are based on single-cell features representation, which fails to fully characterize the agent state, resulting in suboptimal performance. Also, most of such methods lack scalability, due to state-action explosion, and generalization ability. In this paper, we propose a Graph Attention Q-learning (GAQ) algorithm for tilt optimization. GAQ relies on a graph attention mechanism to select relevant neighbors information, improve the agent state representation, and update the tilt control policy based on a history of observations using a Deep Q-Network (DQN). We show that GAQ efficiently captures important network information and outperforms standard DQN with local information by a large margin. In addition, we demonstrate its ability to generalize to network deployments of different sizes and densities.