On-demand deployment of small cells plays a key role in augmenting macro-cell coverage for outdoor hotspots, where user devices are brought together and intensively upload self-generated data. In this paper, we study spectrum sharing among rapidly deployable small cells in the uplink, even without a priori global knowledge. We propose a hybrid multi-agent approach, which allows a leading macro-cell base station (MBS) and multiple following small base stations (SBSs) to take part in a user-centric, online joint optimization of small cell deployment and uplink resource allocation. Specifically, we propose a centralized mechanism for the MBS to solve the first subproblem of small cell deployment stage by stage, based on an adversarial bandit model. Furthermore, we propose a distributed mechanism for the group of SBSs to collectively solve the second subproblem of uplink resource allocation stage by stage, based on a stochastic game model. We prove that our approach is guaranteed to produce a joint strategy, which is built upon a mixed strategy with bounded regret on the first tier and an equilibrium solution on the second tier. Our approach is validated by simulations on the aspects of convergence behavior, strategy correctness, power consumption, and spectral efficiency.

中文翻译：

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可快速部署的小蜂窝之间的频谱共享：混合多代理方法

小基站的按需部署在扩大室外热点的宏小区覆盖方面起着关键作用，用户设备聚集在一起并集中上传自生成的数据。在本文中，我们研究了上行链路中可快速部署的小蜂窝之间的频谱共享，即使没有先验的全局知识。我们提出了一种混合多代理方法，它允许领先的宏小区基站（MBS）和多个跟随的小基站（SBS）参与以用户为中心的小小区部署和上行链路资源分配的在线联合优化. 具体来说，我们提出了一种基于对抗性强盗模型的集中式机制，让 MBS 逐步解决小小区部署的第一个子问题。此外，基于随机博弈模型，我们提出了一种分布式机制，由一组 SBS 共同解决上行资源分配的第二个子问题。我们证明我们的方法可以保证产生一个联合策略，该策略建立在混合策略的基础上，第一层有有限的遗憾，第二层是均衡解决方案。我们的方法通过在收敛行为、策略正确性、功耗和频谱效率方面的模拟得到验证。