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Energy efficiency (EE) is one of the most important metrics for 5G and future6G networks to reduce energy costs and control carbon footprint. Sleep control,as a cost-efficient approach, can significantly lower power consumption byswitching off network devices selectively. Meanwhile, reconfigurableintelligent surface (RIS) has emerged as a promising technique to enhance theEE of 5G beyond and 6G networks. In this work, we jointly consider sleep andtransmission power control for reconfigurable intelligent surface (RIS)-aidedenergy-efficient heterogeneous networks (Hetnets). In particular, we firstpropose a fractional programming (FP) method for RIS phase-shift control, whichaims to maximize the sum-rate under given transmission power levels. Then,considering the timescale difference between sleep control and power control,we introduce a cooperative hierarchical deep reinforcement learning (Co-HDRL)algorithm, including a cross-entropy enabled meta-controller for sleep control,and correlated equilibrium-based sub-controllers for power control. Moreover,we proposed a surrogate optimization method as one baseline for RIS control,and conventional HDRL as another baseline for sleep and power control. Finally,simulations show that the RIS-assisted sleep control can achieve more than 16%lower energy consumption and 30% higher energy efficiency than baselinealgorithms.

Cooperative Hierarchical Deep Reinforcement Learning based Joint Sleep, Power, and RIS Control for Energy-Efficient HetNet