Context-oriented performance evaluation of network selection algorithms in 5G heterogeneous networks

Abstract

Vertical handover is the essential feature of the next generation of heterogeneous wireless networks. Network selection among accessible candidates is the most critical step in vertical handover procedure. Although various algorithms for network selection have been proposed during recent years, there is no comprehensive study to consider all subscribers’ context and requirements in a systematic structured manner. Furthermore, due to different approaches and variety of parameters related to this complicated problem, no inclusive benchmark exists to evaluate the performance of the algorithms. In this paper, we define a global user satisfaction indicator (USI) as a quantitative metric which comprehensively qualifies all candidate networks based on all subscribers’ contexts. Our method is inspired by analytical hierarchical process (AHP) and utility function theory which systematically integrates both customer and network side parameters. This idea leads to proposing a novel algorithm called opportune context-aware network selection (OCANS). OCANS is a network-assisted user centric approach which dynamically and automatically takes into account customer context in the decision of the best network to achieve maximum user satisfaction. In addition to analytic computations, our practical simulation framework evaluates and compares OCANS to well-known network selection algorithms such as simple additive weighting (SAW), technique for order preference by similarity to ideal solution (TOPSIS) and Multiplicative Exponential Weighting (MEW). Simulation results along with analytic computations show that our proposed algorithm not only leads to higher USI, but also is aligned to customer preferences and has superior performance in desired indicators such as number of handovers, security grade, quality of service (QoS), energy consumption and service cost.