Fuzzy neural network based access selection in satellite–terrestrial integrated networks

Access selection has become a significant problem in satellite–terrestrial integrated networks (STINs) to determine the most suitable network. Existing solutions fail to solve the complexity and diversity challenges when user preferences are considered. In this study, the access selection problem in satellite–terrestrial integrated networks is considered, and user preferences for different network types are incorporated into the access selection decision-making process. This paper introduces fuzzy neural network (FNN) for access selection in STINs and contributes an improved FNN model with the African Vulture optimization algorithm to solve the access selection problem, which is proven to be better than the three sophisticated baselines in terms of convergence speed, blocking rate, system throughput, and user satisfaction. Compared with the traditional Fuzzy-Logic baseline, the proposed FNN model achieves an approximate 8% lower blocking rate, an approximate 40% higher system throughput, and an approximate 8% higher user satisfaction with an arrival rate of 10 requests per second in numerical experiments.