Dynamic network selection in HetNets: A social-behavioral (SoBe) approach

Abstract

In this paper, we propose a two-layer game-theoretic framework to solve the network selection problem in heterogeneous wireless networks (HetNets). At the intra-network layer, a hierarchical game among the SP and its admitted users is employed, and the closed-form equilibrium solutions for the pricing and transmission rate are provided. Meanwhile, at the inter-network layer, all service providers (SP) and the active users are engaged in a dynamic hedonic game and finally self-organized into a Nash-stable coalition structure. The key feature of our proposed approach is the inclusion of socialbehavioral (SoBe) constraints capturing the real-world user and SP preferences, hierarchy and membership. Not only that SoBe more accurately models practical scenarios, it also leads to markedly reduced unnecessary handovers, and well maintained call blocking rate. Simulations confirm the superior performance of SoBe in comparison with the widely adopted user-driven alternative in terms of nearly all critical performance criteria.

Cell Association With User Behavior Awareness in Heterogeneous Cellular Networks

“…They systematically review the studies of user association in HetNets, massive MIMO networks, mmWave networks, and energy harvesting networks. Focusing on HetNets, most proposed cell association policies determine the access BS without considering user behaviors [4]- [9], while a little work takes it into account [18]- [20]. Indeed, only [20] explicitly exploits user behavior for the network selection in heterogeneous wireless networks.…”

“…to improve network performance. In addition, a few researchers begin to consider user behaviors in cell association [18]- [20]. The authors of [18] study handoff in HetNets by considering user behaviors such as UEs movement trajectory, position and speed.…”

“…In [19], the authors propose a machine learning based cell association algorithm in HetNets in a predictable horizon. To the best of our knowledge, only the authors of [20] explicitly exploit the characteristics of individual user behaviors for network selection in heterogeneous wireless networks, consisting of LTE and Wi-Fi. They propose a two-layer game-theoretic framework to solve the problem by considering individual user behavior with aim to reduce unnecessary handoffs.…”

“…We next briefly describe the derivation process from (14) to (20). We first introduce new variables x an jn defined in (16).…”

“…From the definition of x an jn , the optimization objective (14) can be transformed into (20). Note that the given condition S(0) in (14) is included as constraint (20-1).…”