On the Optimization of User Association and Resource Allocation in HetNets With mm-Wave Base Stations

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.