干扰耦合被动冷却基站智能热管理在线学习

Zhanwei Yu;Yi Zhao;Xiaoli Chu;Di Yuan
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摘要

被动冷却基站(PCBSs)已经出现,以提供更好的成本和能源效率。然而,被动冷却需要通过流量管理进行智能热控制,即pcb的瞬时数据流量或吞吐量直接影响其热性能。这对于pcb的户外部署尤其具有挑战性,因为散热效率是不确定的,并且随着时间的推移而波动。更重要的是,pcb在多单元场景中是干扰耦合的。因此,更高吞吐量的pcb会导致对其他pcb的更高干扰,这反过来又需要更多的资源消耗来满足各自的吞吐量目标。在本文中,我们讨论了在线决策,以最大限度地提高受工作温度限制的多pcb系统的总下行吞吐量。我们证明了一种强化学习(RL)方法,特别是软行为者批评(SAC),可以通过使吞吐量适应时变的散热条件,在保持pcb冷却的同时成功地实现吞吐量最大化。此外,我们设计了一个拒绝和奖励机制,有效地降低了RL探索阶段过热的风险。仿真结果表明,该方法达到了全局最优解的88.6%。这是非常有希望的,因为我们的方法在没有全局最优所要求的未来散热效率的先验知识的情况下运行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Learning for Intelligent Thermal Management of Interference-Coupled and Passively Cooled Base Stations
Passively cooled base stations (PCBSs) have emerged to deliver better cost and energy efficiency. However, passive cooling necessitates intelligent thermal control via traffic management, i.e., the instantaneous data traffic or throughput of a PCBS directly impacts its thermal performance. This is particularly challenging for outdoor deployment of PCBSs because the heat dissipation efficiency is uncertain and fluctuates over time. What is more, the PCBSs are interference-coupled in multi-cell scenarios. Thus, a higher-throughput PCBS leads to higher interference to the other PCBSs, which, in turn, would require more resource consumption to meet their respective throughput targets. In this paper, we address online decision-making for maximizing the total downlink throughput for a multi-PCBS system subject to constraints related on operating temperature. We demonstrate that a reinforcement learning (RL) approach, specifically soft actor-critic (SAC), can successfully perform throughput maximization while keeping the PCBSs cool, by adapting the throughput to time-varying heat dissipation conditions. Furthermore, we design a denial and reward mechanism that effectively mitigates the risk of overheating during the exploration phase of RL. Simulation results show that our approach achieves up to 88.6% of the global optimum. This is very promising, as our approach operates without prior knowledge of future heat dissipation efficiency, which is required by the global optimum.
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