基于强化学习的非聚集绿色开放RAN动态函数分裂
Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs
摘要：
随着围绕开放RAN（O-RAN）倡议的增长势头，以有效的方式在分散和虚拟化的无线电接入网络（vRAN）中执行动态功能拆分（FS）变得非常重要。RAN硬件和软件的能耗方面也出现了同样重要的效率需求。为RAN提供可再生能源（RESs）有望提高能源效率。然而，在这样的动态设置中的FS要求能够适应RES供应的变化条件和移动网络上的业务负载的智能机制。在本文中，我们提出了一种基于强化学习（RL）的动态函数分裂（RLDFS）技术，该技术决定O-RAN中的函数分裂，以充分利用RES供应并最小化运营商成本。我们还制定了一个运营支出最小化问题。我们评估所提出的方法对太阳辐射和交通速率变化的真实数据集的性能。我们的结果表明，所提出的RLDFS方法有效地利用了RES并降低了MNO的成本。我们还调查了太阳能电池板和电池的大小的影响，这可能会指导移动网络运营商决定适当的RES和电池大小为他们的网络。
英文摘要：
With the growing momentum around Open RAN (O-RAN) initiatives, performing dynamic Function Splitting (FS) in disaggregated and virtualized Radio Access Networks (vRANs), in an efficient way, is becoming highly important. An equally important efficiency demand is emerging from the energy consumption dimension of the RAN hardware and software. Supplying the RAN with Renewable Energy Sources (RESs) promises to boost the energy-efficiency. Yet, FS in such a dynamic setting, calls for intelligent mechanisms that can adapt to the varying conditions of the RES supply and the traffic load on the mobile network. In this paper, we propose a reinforcement learning (RL)-based dynamic function splitting (RLDFS) technique that decides on the function splits in an O-RAN to make the best use of RES supply and minimize operator costs. We also formulate an operational expenditure minimization problem. We evaluate the performance of the proposed approach on a real data set of solar irradiation and traffic rate variations. Our results show that the proposed RLDFS method makes effective use of RES and reduces the cost of an MNO. We also investigate the impact of the size of solar panels and batteries which may guide MNOs to decide on proper RES and battery sizing for their networks.
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。
