| Citation: | Qianbin CHEN, Qi TAN, Yannan WEI, Lanqin HE, Lun TANG. Dynamic Resource Allocation and Energy Management Algorithm for Hybrid Energy Supply in Heterogeneous Cloud Radio Access Networks[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1428-1435. doi: 10.11999/JEIT190499
|
Considering the dynamic resource allocation and energy management problem in the 5G Heterogeneous Cloud Radio Access Networks(H-CRANs) architecture for hybrid energy supply, a dynamic network resource allocation and energy management algorithm based on deep reinforcement learning is proposed. Firstly, due to the volatility of renewable energy and the randomness of user data service arrival, taking into account the stability of the system, the sustainability of energy and the Quality of Service(QoS) requirements of users, the resource allocation and energy management issues in the H-CRANs network as a Constrained infinite time Markov Decision Process (CMDP) are modeled with the goal of maximizing the average net profit of service providers. Then, the Lagrange multiplier method is used to transform the proposed CMDP problem into an unconstrained Markov Decision Process (MDP) problem. Finally, because the action space and the state space are both continuous value sets, the deep reinforcement learning is used to solve the above MDP problem. The simulation results show that the proposed algorithm can effectively guarantee the QoS and energy sustainability of the system, while improving the average net income of the service provider and reducing energy consumption.
|
彭木根, 艾元. 异构云无线接入网络: 原理、架构、技术和挑战[J]. 电信科学, 2015, 31(5): 41–45.
PENG Mugen and AI Yuan. Heterogeneous cloud radio access networks: Principle, architecture, techniques and challenges[J]. Telecommunications Science, 2015, 31(5): 41–45.
|
|
ALNOMAN A, CARVALHO G H S, ANPALAGAN A, et al. Energy efficiency on fully cloudified mobile networks: Survey, challenges, and open issues[J]. IEEE Communications Surveys & Tutorials, 2018, 20(2): 1271–1291. doi: 10.1109/COMST.2017.2780238
|
|
AKTAR M R, JAHID A, AL-HASAN M, et al. User association for efficient utilization of green energy in cloud radio access network[C]. 2019 International Conference on Electrical, Computer and Communication Engineering, Cox’sBazar, Bangladesh, 2019: 1–5. doi: 10.1109/ECACE.2019.8679128.
|
|
ALQERM I and SHIHADA B. Sophisticated online learning scheme for green resource allocation in 5G heterogeneous cloud radio access networks[J]. IEEE Transactions on Mobile Computing, 2018, 17(10): 2423–2437. doi: 10.1109/TMC.2018.2797166
|
|
LIU Qiang, HAN Tao, ANSARI N, et al. On designing energy-efficient heterogeneous cloud radio access networks[J]. IEEE Transactions on Green Communications and Networking, 2018, 2(3): 721–734. doi: 10.1109/TGCN.2018.2835451
|
|
吴晓民. 能量捕获驱动的异构网络资源调度与优化研究[D]. [博士论文], 中国科学技术大学, 2016.
WU Xiaomin. Resources optimization and control in the energy harvesting heterogeneous network[D]. [Ph.D. dissertation], University of Science and Technology of China, 2016.
|
|
ZHANG Deyu, CHEN Zhigang, CAI L X, et al. Resource allocation for green cloud radio access networks with hybrid energy supplies[J]. IEEE Transactions on Vehicular Technology, 2018, 67(2): 1684–1697. doi: 10.1109/TVT.2017.2754273
|
|
孔巧. 混合能源供能的异构蜂窝网络中能源成本最小化问题的研究[D]. [硕士论文], 华中科技大学, 2016.
KONG Qiao. Research on energy cost minimization problem in heterogeneous cellular networks with hybrid energy supplies[D]. [Master dissertation], Huazhong University of Science and Technology, 2016.
|
|
YANG Jian, YANG Qinghai, SHEN Zhong, et al. Suboptimal online resource allocation in hybrid energy supplied OFDMA cellular networks[J]. IEEE Communications Letters, 2016, 20(8): 1639–1642. doi: 10.1109/LCOMM.2016.2575834
|
|
WEI Yifei, YU F R, SONG Mei, et al. User scheduling and resource allocation in HetNets with hybrid energy supply: An actor-critic reinforcement learning approach[J]. IEEE Transactions on Wireless Communications, 2018, 17(1): 680–692. doi: 10.1109/TWC.2017.2769644
|
|
PENG Mugen, ZHANG Kecheng, JIANG Jiamo, et al. Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks[J]. IEEE Transactions on Vehicular Technology, 2015, 64(11): 5275–5287. doi: 10.1109/TVT.2014.2379922
|
|
陈前斌, 杨友超, 周钰, 等. 基于随机学习的接入网服务功能链部署算法[J]. 电子与信息学报, 2019, 41(2): 417–423. doi: 10.11999/JEIT180310
CHEN Qianbin, YANG Youchao, ZHOU Yu, et al. Deployment algorithm of service function chain of access network based on stochastic learning[J]. Journal of Electronics &Information Technology, 2019, 41(2): 417–423. doi: 10.11999/JEIT180310
|
|
深度强化学习-DDPG算法原理和实现[EB/OL]. https://www.jianshu.com/p/6fe18d0d8822, 2018.
|
|
齐岳, 黄硕华. 基于深度强化学习DDPG算法的投资组合管理[J]. 计算机与现代化, 2018(5): 93–99. doi: 10.3969/j.issn.1006-2475.2018.05.019
QI Yue and HUANG Shuohua. Portfolio management based on DDPG algorithm of deep reinforcement learning[J]. Computer and Modernization, 2018(5): 93–99. doi: 10.3969/j.issn.1006-2475.2018.05.019
|
|
California ISO[EB/OL]. http://www.caiso.com, 2019.
|
|
WANG Xin, ZHANG Yu, CHEN Tianyi, et al. Dynamic energy management for smart-grid-powered coordinated multipoint systems[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(5): 1348–1359. doi: 10.1109/JSAC.2016.2520220
|
|
LI Jian, PENG Mugen, YU Yuling, et al. Energy-efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(12): 9873–9887. doi: 10.1109/TVT.2016.2531184
|
Figures(8) / Tables(2)
DownLoad:
