Combinatorial User Association in Heterogeneous Wireless Networks via a Statistical Representation
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)(2021)
摘要
Future heterogeneous wireless networks (HetNets) should provide massive connectivity for a large number of devices. In such networks, the problem of user association and multiple access management is of paramount importance. This paper formulates a statistical representation of the user association problem using users' probability density function. In other words, this paper is a bridge between the dynamic mixed-integer formulation of the user association used in relaxation, game theory, and reinforcement learning approaches and the static user association considered in analytical stochastic geometry methods. To this aim, a novel representation independent of the number of users is derived from the combinatorial user association formulation. Interestingly, we show that for fair user association, the statistical representation is a multi-objective optimization. The first objective is to maximize the network throughput with fairness consideration, and the second objective is to optimize the load balancing in terms of the Shannon entropy. Based on this representation, we introduce an algorithm to optimize user association using the first-order derivative formula. We propose a method that can optimize individual base stations' bias factors inside each tier of a HetNet. Numerical results show that the statistical representation closely tracks the stochastic behavior of the dynamic association. The proposed optimization approach improves the 10% outage rate of a two-tier network by 19% and enhances the load balancing by reducing the load of macro base stations by 22%.
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关键词
User association, heterogeneous wireless networks, combinatorial optimization, load balancing
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