Fuzzy Multi-Attribute Utility Based Network Selection Approach for High-Speed Railway Scenario

Heterogeneous network selection algorithm for novel 5G services based on evolutionary game

“…However, user preference attributes cannot be accurately obtained. MADM-based network selection algorithms, for instance, simple additive weighting (SAW) [3,4], multiplicative exponent weighting (MEW) [5], grey relational analysis [6], order preference by similarity to ideal solution (TOPSIS) [7] as well as analytic hierarchy process (AHP) [8][9][10][11], rely on experiences to get near-optimal selection, but these methods lack strict theoretical proof of optimality. Machine learning-based algorithms in [12,13] utilise reinforcement learning to get network selection, while they depend too much on the accuracy of the data set and the processor performance.…”

“…• Compared with the existing network selection methods such as SAW [3], MEW [5], Q-learning based algorithm (QBNS) [12], improved multi-agent algorithm based on QBNS (MQBNS) and bipartite graph matching based algorithm (BGA) [26], NS-EG can not only improve average satisfaction and EE user experienced but also reduce average delay and overall PLR in 5G service scenarios with the increasing number of users.…”