Handover algorithm for space-air-ground integrated network based on location prediction model

To address the issues of frequent handovers and network load imbalance caused by dynamic changes in the network environment and enhanced mobility of user terminals in the 6G space-air-ground integrated network (SAGIN), a handover algorithm for SAGIN based on a terminal location prediction model was proposed. The algorithm constructed a long short-term memory (LSTM) network terminal location prediction model optimized based on the sparrow search strategy, improving the accuracy of terminal location prediction and resolving the issue of unreasonable handover timing. Based on this model, the SAGIN selection problem was modeled as a Markov decision process. A network handover algorithm utility function characterized by quality of service (QoS) requirements, handover cost, and network load balancing was designed. A distributional deep Q-network (D-DQN) was employed to select the network nodes that could maximize long-term goals for execution handover. Compared with network handover algorithms based on Q-Learning, double deep Q-network (DDQN), and dueling double deep Q-network (D3QN), the proposed algorithm performs better in terms of reducing handover delay and frequency, as well as enhancing network throughput, thereby validating the effectiveness of the proposed algorithm.