Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.

中文翻译：

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使用强化学习框架进行多小区LTE-U / Wi-Fi共存评估。

蜂窝宽带物联网（IoT）应用预计将保持逐年增长的态势，从而产生高吞吐量服务的需求。由于其中一些应用程序是通过长期演进（LTE）部署在许可的移动网络上的，因此已经面临一个普遍的问题：许可频谱的稀缺性无法满足对数据速率不断增长的需求。旨在解决此问题的LTE非授权（LTE-U）论坛建议LTE-U在5 GHz非授权频谱中运行。但是，除了非授权频段的新技术需要促进与传统技术公平共存的机制这一事实之外，Wi-Fi已经是在这一部分频谱中运行的整合技术。在这项工作中，我们通过分析多小区LTE-U / Wi-Fi共存场景来扩展文献，具有针对蜂窝宽带IoT部署的高干扰曲线和数据速率。然后，我们提出了一个集中的，协调的强化学习框架，以提高LTE-U / Wi-Fi聚合数据速率。ns-3仿真器评估了所提出解决方案的附加价值，即使在多小区环境中存在高干扰的情况下，该解决方案不仅显示了整体系统数据速率的提高，而且显示了平均用户数据速率的提高。