A Graph Attention plus Reinforcement Learning Method for Antenna Tilt Optimization
Abstract: Remote Electrical Tilt optimization is an effective method to obtain the optimal Key Performance Indicators (KPIs) by remotely controlling the base station antenna’s vertical tilt. To improve the KPIs aims to improve antennas’ cooperation effect since KPIs measure the quality of cooperation between the antenna to be optimized and its neighbor antennas. Reinforcement Learning (RL) is an appropriate method to learn an antenna tilt control policy since the agent in RL can generate the optimal epsilon greedy tilt optimization policy by observing the environment and learning from the state- action pairs. However, existing models only produced tilt modification strategies by interpreting the to- be- optimized antenna’s features, which cannot fully characterize the mobile cellular network formed by the to- be- optimized antenna and its neighbors. Therefore, incorporating the features of the neighboring antennas into the model is an important measure to improve the optimization strategy. This work will introduce the Graph Attention Network to model the neighborhood antenna’s impact on the antenna to be optimized through the attention mechanism. Furthermore, it will generate a low- dimensional embedding vector with more expressive power to represent the to- be- optimized antenna’s state in the RL framework through dealing with graph- structural data. This new model, namely Graph Attention Q- Network (GAQ), is a model based on DQN and aims to acquire a higher performance than the Deep Q- Network (DQN) model, which is the baseline, evaluated by the same metric — KPI Improvement. Since GAQ has a richer perception of the environment than the vanilla DQN model, it thereby outperforms the DQN model, obtaining fourteen percent performance improvement compared to the baseline. Besides, GAQ also performs 14 per cent better than DQN in terms of convergence efficiency.
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