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Joint user association and resource allocation in HetNets based on user mobility prediction

机译:基于用户移动预测的Hetnets联合用户关联和资源分配

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摘要

Virtual small cell (VSC) formed by directional beams is seen as an alternative for the small base station (SBS) within the coverage of macro base station (MBS), to increase system capacity and reduce site cost. However, the flexibility of development for VSC poses challenges to user association and resource allocation in heterogeneous networks. In this paper, we consider the joint problem of user association and resource allocation in VSC aided multi-tier heterogeneous networks. To better analyze the formation of VSC and the impact of user mobility on the system, a user mobility prediction model is firstly constructed based on the Markov model. Then the joint user association and resource allocation problem is formulated to maximize the system capacity. Since the aforementioned problem is a coupling problem, two different solutions, namely, a decoupling solution and a coupling solution, are proposed based on the multi-agent Q-learning (MAQL) method, to find the optimal user association and resource allocation strategy. Moreover, to overcome the state and action space explosion in MAQL and accelerate convergence, the deep Q-network (DQN) is applied. Simulation results reveal that the deployment of VSC can increase the system capacity and spectrum efficiency. The coupling solution achieves better performance than the decoupling solution under a large number of users.
机译:通过定向光束形成的虚拟小单元(VSC)被视为宏基站(MBS)覆盖范围内的小型基站(SBS)的替代方案,以提高系统容量并降低现场成本。然而,VSC的开发的灵活性对用户关联和异构网络中的资源分配构成了挑战。在本文中,我们考虑了VSC辅助多层异构网络中用户关联和资源分配的联合问题。为了更好地分析VSC的形成和用户移动性对系统的影响,首先基于Markov模型构建用户移动预测模型。然后,配制联合用户关联和资源分配问题以最大化系统容量。由于上述问题是耦合问题,基于多代理Q学习(MAQL)方法提出了两个不同的解决方案,即解耦解决方案和耦合解决方案,以找到最佳用户关联和资源分配策略。此外,为了克服MAQL中的状态和动作空间爆炸并加速收敛,应用了深Q网络(DQN)。仿真结果表明,VSC的部署可以提高系统容量和频谱效率。耦合解决方案在大量用户下实现比解耦解决方案更好。

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