This article investigates the problem of joint user association and resource allocation, defined by the number of allocated time-slots, in hybrid heterogeneous networks with the coexistence of sub-6-GHz base stations and millimeter wave (mm-Wave) base stations. To do so, we formulate a joint optimization problem to improve the efficiency of resource utilization by maximizing the number of associated users and minimizing the number of allocated time-slots. The optimization problem is formulated as a binary integer linear program and is proved to be NP-hard. Accordingly, we propose two efficient heuristic algorithms to solve it. The first one is centralized and relies on complete information, whereas the second one is distributed and is based on a reinforcement learning approach. The proposed distributed learning algorithm aims to find the best association for each user based on its past experience, automatically and independently from others. Simulation results show that the performances of both proposed algorithms are close-to-optimal with an important reduction in computational complexity.

中文翻译：

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毫米波基站在HetNet中用户关联和资源分配的优化

本文研究了混合异构网络中低于6 GHz基站和毫米波（mm-Wave）基站并存的联合用户关联和资源分配问题，该问题由分配的时隙数定义。为此，我们提出了一个联合优化问题，以通过最大化关联用户数量和最小化分配的时隙数量来提高资源利用效率。该优化问题用二进制整数线性程序表示，并且证明是NP难的。因此，我们提出了两种有效的启发式算法来解决它。第一个是集中式的，并依赖于完整的信息，而第二个是分布式的，并基于强化学习方法。所提出的分布式学习算法旨在根据每个用户的过去经验自动，独立于其他用户找到最佳关联。仿真结果表明，两种算法的性能都接近最佳，并且计算复杂度大大降低。