Proactive handover optimization via multi‐agent deep reinforcement learning in integrated LEO satellite–terrestrial networks for high‐mobility terminals

Abstract This paper proposes a predictive handover decision framework for optimizing handover planning in integrated low Earth orbit (LEO) satellite–terrestrial networks. To forecast the fluctuations in received signal reference power (RSRP) caused by the high mobility of mobile terminals (MTs) and satellites accurately, we employ a convolutional neural network–long short‐term memory encoder–decoder architecture. This forecasting model enables each MT to predict its future RSRP levels independently over a given time horizon. By leveraging the predicted RSRP trajectories, each MT autonomously determines its handover strategy, including its optimal target and timing. To enhance the performance of these decentralized handover decisions, we integrate a multi‐agent deep reinforcement learning approach based on the QMIX algorithm. This approach enables coordinated policy learning among MT agents while preserving decentralized execution, thereby improving overall system stability and handover efficiency in highly dynamic environments.