论文标题
基于增强学习的动态功能在分解的绿色开放式架中分裂
Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs
论文作者
论文摘要
随着开放式RAN（O-RAN）计划的增长，以有效的方式进行分解和虚拟化无线电访问网络（VRAN）的动态功能分裂（FS）变得非常重要。同样重要的效率需求是从运行硬件和软件的能耗维度出现。为运行提供可再生能源（RES）有望提高能源效率。但是，在这种动态设置中，FS需要智能机制，以适应移动网络上RES供应和流量负载的不同条件。在本文中，我们提出了增强学习（RL）基于动态功能拆分（RLDFS）技术，该技术决定了O-RAN功能分割的功能，以充分利用RES供应并最大程度地减少运营商的成本。我们还制定了运营支出最小化问题。我们在太阳照射和交通率变化的真实数据集上评估了提出方法的性能。我们的结果表明，提出的RLDFS方法有效地利用了RES并降低了MNO的成本。我们还研究了太阳能电池板和电池的大小的影响，这些太阳能电池板和电池可能会指导MNO为其网络决定适当的RES和电池尺寸。
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.
