Heterogeneous Network Selection Algorithm Based on Principal Component Analysis

Abstract

Multiple attribute decision making (MADM) methods have shown effectiveness for network selection of heterogeneous network system, and analytic hierarchy process (AHP) is often applied to determine subjective attribute weights. However, APH method needs constructing appropriate judgment matrix and cannot address different business scenes. In this paper, we propose a novel selection algorithm for heterogeneous network system by introducing principal component analysis (PCA). By locating key attributes of different subjective demands, the whole loading vectors of PCA model can be divided into several blocks to fit different scenes. Then, the loading vectors can be used to construct subjective weights. Furthermore, we apply Criteria importance though intercrieria correlation (CITIC) method based objective weight to construct a combined weight. Therefore, the subjective demand and network objective feature can be considered jointly to select the optimal network. Simulation results show that the proposed approach can select the optimal network for different business scenes.