Ultra-dense deployments in 5G, the next generation of cellular networks, are an alternative to provide ultra-high throughput by bringing the users closer to the base stations. On the other hand, 5G deployments must not incur a large increase in energy consumption in order to keep them cost-effective and most importantly to reduce the carbon footprint of cellular networks. We propose a reinforcement learning cell switching algorithm, to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed algorithm can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed algorithm can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex.

中文翻译：

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通过流量感知小区交换在超密集无线接入网络中进行能量优化

下一代蜂窝网络 5G 中的超密集部署是通过使用户更靠近基站来提供超高吞吐量的替代方案。另一方面，5G 部署不得导致能耗大幅增加，以保持其成本效益，最重要的是减少蜂窝网络的碳足迹。我们提出了一种强化学习单元切换算法，以在不影响用户体验的服务质量 (QoS) 的情况下最大限度地减少超密集部署中的能耗。在这方面，所提出的算法可以基于SC和宏小区的业务负载智能地获知在任何给定时间关闭哪些小小区(SC)。为了验证这个想法，我们使用了来自意大利米兰市的公开通话详细记录 (CDR) 数据集，并针对典型的操作基准解决方案测试了我们的算法。通过获得的结果，我们准确地展示了所提出的算法何时以及如何提供节能，以及如何在不降低用户 QoS 的情况下实现这一点。最重要的是，我们表明我们的解决方案具有与穷举搜索非常相似的性能，优势在于可扩展且不那么复杂。