A network selection algorithm for space-air-ground integrated network based on location prediction model and multi-attribute decision making

As an indispensable component of 5G and even the future 6G networks, the Space-Air-Ground Integrated Network (SAGIN) is envisioned to provide ubiquitous network connectivity and services by integrating satellite, aerial, and terrestrial networks. However, due to the frequent network selection of in-vehicle terminals, the user’s Quality of Service (QoS) can significantly deteriorate. To address this issue, a network selection algorithm based on terminal location prediction has been proposed. Firstly, we enhanced the Particle Swarm Optimization (PSO) algorithm to optimize the hyper-parameters of the Long Short-Term Memory (LSTM) network, thereby improving the accuracy of terminal location prediction. After constructing the network sets of the current terminal position and the predicted position, respectively, we designed a network selection judgment mechanism with a dynamically adjustable switching threshold based on Fuzzy Logic and K-Means theory. Finally, through the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) algorithm, we have achieved robust network selection in fast-moving scenarios. The simulation results show that the proposed algorithm can adaptively adjust the switching threshold and provide precise positions. Compared to existing algorithms, it can significantly reduce the number of candidate networks and the number of selections, thereby reducing the computational load and increasing the throughput of users.