An AI-Based Optimization of Handover Strategy in Non-Terrestrial Networks

A complicated radio resource management, e.g., handover condition, will be suffered by the user in non-terrestrial networks due to the impact of high mobility and hierarchical layouts which co-exist with terrestrial networks or various platforms at different altitudes. It is necessary to optimize the handover strategy to reduce the signaling overhead and improve the service continuity. In this paper, a new handover strategy is proposed based on the convolutional neural network. Firstly, the handover process is modeled as a directed graph. Suppose a user knows its future signal strength, then it can search for the best handover strategy based on the graph. Secondly, a convolutional neural network is used to extract the underlying regularity of the best handover strategies of different users, based on which any user can make near-optimal handover decision according to its historical signal strength. Numerical simulation shows that the proposed handover strategy can efficiently reduce the handover number while ensuring the signal strength.