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OACSCDCSTPCD

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.