在性能约束下通过贝叶斯学习关闭载波实现节能
Energy savings under performance constraints via carrier shutdown with Bayesian learning
摘要：
通过关闭频率载波，基站消耗的功率可以显著降低。然而，这通常伴随着业务性能降级，因为剩余活动载波上的拥塞增加。我们利用滞后载波关闭政策，试图保持每个扇区的平均流量负载在一定的最小/最大阈值对。我们提出了一个闭环贝叶斯方法优化这样的阈值的扇区的基础上，旨在最大限度地减少功率放大器消耗的功率，同时保持KPI的概率是可以接受的高于一定的值。我们在现场客户4G网络中测试了我们的方法。基站的功耗降低了11%，选定的KPI达到了预定义的目标。
英文摘要：
By shutting down frequency carriers, the power consumed by a base station can be considerably reduced. However, this typically comes with traffic performance degradation, as the congestion on the remaining active carriers is increased. We leverage a hysteresis carrier shutdown policy that attempts to keep the average traffic load on each sector within a certain min/max threshold pair. We propose a closed-loop Bayesian method optimizing such thresholds on a sector basis and aiming at minimizing the power consumed by the power amplifiers while maintaining the probability that KPI's are acceptable above a certain value. We tested our approach in a live customer 4G network. The power consumption at the base station was reduced by 11% and the selected KPI's met the predefined targets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
