User Association in Heterogeneous Networks: A Social Interaction Approach

This paper deals with the problem of user association in heterogeneous networks (HetNets). With the existence of small cells, to better associate user equipment (UE) with base stations (BSs), we propose a novel collaborative filtering (CF)-based wireless network recommendation system, which involves social interactions among UEs. Different from traditional user association schemes, the UEs in the proposed system interact with each other by rating the BSs to improve recommendation qualities. Similarity between UEs' preferences to the quality of service (QoS) and historical information on the QoS of networks are taken into consideration. The core of the network recommendation system is a rating matrix maintained by the operator. A UE can automatically rate the connected BS according to real-time measured parameters (interference, packet loss rate, time delay, etc.) that it experiences during service. Taking the Voice over Internet Protocol (VoIP) service as an example, the measured parameters can be mapped to certain QoS levels with the E-model, which is used as the ratings. During the process of handover, the operator recommends the BSs to the UEs based on the rating matrix. While the ratings help the UEs gain better recommendation qualities, they tend to avoid the costs, e. g., computational and bandwidth resources. Therefore, the UEs have to trade off between the profits and costs brought about by the rating procedure. A satisfaction game is formulated to deal with this problem. We use a utility function to measure a UE's satisfaction. When every UE's utility comes to a preset level, the game is considered to reach the satisfaction equilibrium(SE). An algorithm is designed to learn the SE, and the convergence is analyzed. The advantages of the proposed system lie in two aspects. First, it comprehensively considers multiple factors that influence the QoS, instead of signal strength only. Second, the historical information helps UEs select BSs with better long-term QoS but not immediate QoS. Simulation results show the effectiveness of the learning algorithm on both learning the SE and avoiding severe congestions in each BS.