随着5G移动互联网的快速发展，为了满足用户不断增长的流量需求，5G基站大规模部署，导致能耗急剧增加。针对以上问题，通过采用流量预测与深度强化学习相结合的方法，建立基站动态开关模型。具体而言，该模型通过密集卷积神经网络(Densely Connected Convolutional Networks，DenseNet)对基站流量进行预测;进一步地，基于精确的移动流量预测，将基站开关控制问题建模为一个马尔科夫决策过程(Markov Decision Process，MDP)，然后通过强化学习方法进行求解。此外，强化学习的reward函数设计在优化基站开关成本时综合考虑了多方面的因素，包括能耗和用户服务质量(Quality of Service，QoS)下降成本，目标是在降低能耗的前提下，最小化长期的基站能量消耗。最终通过对真实数据集的大量实验验证，提出的模型与当前使用的基站常开策略相比，能够节约37％的能量消耗，且节能效果也优于传统启发式算法。
Research on Dynamic Base Station Switching Based on Deep Reinforcement Learning
With the rapid development of 5G mobile internet,the large-scale deployment of 5G base stations to meet the ever-grow-ing demand for data traffic has led to a sharp increase in energy consumption.To address this issue,a dynamic base station switching model is established by combining traffic prediction with deep reinforcement learning.Specifically,this model uses a densely connected convolutional neural network to predict base station traffic.Further,based on accurate mobile traffic predictions,the base station switc-hing control problem is modeled as a Markov Decision Process(MDP),and solved using reinforcement learning methods.Moreover,the reward function design in reinforcement learning comprehensively considers multiple factors in optimizing base station switching costs,including energy consumption and the cost of degrading Quality of Service(QoS).The objective is to minimize long-term base station energy consumption while reducing energy use,without compromising QoS.Extensive experiments on real datasets demonstrate that the proposed model can save 37％of energy consumption compared to the current keep-on strategy for base stations,and its ener-gy-saving performance also surpasses traditional heuristic algorithms.
base stationcellular networkdynamic switchtraffic predictionDQN
国家科技期刊平台
NSTL
万方数据
