Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this context, designing low-complexity policies with local observations, yet able to adapt the user association with respect to the global network state and to the network dynamics is a challenge. In fact, the frameworks proposed in literature require continuous access to global network information and to recompute the association when the radio environment changes. With the complexity associated to such an approach, these solutions are not well suited to dense 5G networks. In this paper, we address this issue by designing a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. In this approach, users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Since there is no direct information exchange among the agents, we also limit the signaling overhead. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.

中文翻译：

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动态毫米波网络中自适应用户关联的多代理强化学习

网络密集化和毫米波技术是满足第五代 (5G) 移动网络容量和数据速率要求的关键推动因素。在这种情况下，设计具有局部观察的低复杂性策略，但能够根据全局网络状态和网络动态调整用户关联是一个挑战。事实上，文献中提出的框架需要持续访问全球网络信息并在无线电环境变化时重新计算关联。由于这种方法的复杂性，这些解决方案不太适合密集的 5G 网络。在本文中，我们通过设计一种基于多智能体强化学习的可扩展且灵活的用户关联算法来解决这个问题。在这种方法中，用户充当独立代理，仅基于他们的本地观察，学习自主协调他们的行动以优化网络总和率。由于代理之间没有直接的信息交换，我们也限制了信令开销。仿真结果表明，所提出的算法能够适应无线电环境的（快速）变化，从而与最先进的解决方案相比提供大的总速率增益。