电子学报2023，Vol.51Issue(10)：2831-2843,13.DOI:10.12263/DZXB.20220247
无蜂窝毫米波大规模MIMO系统基于深度强化学习的节能睡眠策略
Energy-Efficient Sleep-Mode Based on Deep Reinforcement Learning for Cell-Free mmWave Massive MIMO Systems
摘要
Abstract
To improve the global energy-efficiency(GEE)performance in cell-free millimeter-wave massive MIMO(CF mmWave mMIMO)systems,the access points(APs)sleep-mode techniques in dynamic time-varying channels are in-vestigated.The AP switch ON-OFF(ASO)strategy is formulated as a Markov decision process.Thus,a deep reinforce-ment learning(DRL)model can be used to solve the AP activation problem.The interference-aware method and the locali-ty-sensitive hashing method are introduced to reduce sample bias and interaction between agents and complex environ-ments.A novel cost function is constructed to achieve a better balance between GEE and achievable rate under the strict quality of service(QoS)constraints.In order to accelerate the convergence of the dueling deep Q-Network(DQN),the state space is mapped to the smaller hierarchical state space by discretizing the cost function.Simulation results have dem-onstrated the performance advantage of the convergence of deep reinforcement learning and GEE under the strict QoS con-straint.关键词
无蜂窝/毫米波/深度强化学习/AP开关切换/能效Key words
cell-free/millimeter-wave/deep reinforcement learning/access point switch on-off/energy-efficiency分类
信息技术与安全科学引用本文复制引用
何云,申敏,王蕊,张梦..无蜂窝毫米波大规模MIMO系统基于深度强化学习的节能睡眠策略[J].电子学报,2023,51(10):2831-2843,13.基金项目
国家科技重大专项基金(No.2018ZX03001026-002)National Science and Technology Major Project of China(No.2018ZX03001026-002) （No.2018ZX03001026-002）
