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arXiv 2301.02771eess.SP
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基于层次强化学习的RIS辅助节能RAN

Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN

Hao Zhou, Long Kong, Medhat Elsayed, Majid Bavand, Raimundas Gaigalas, Steve Furr, Melike Erol-Kantarci

摘要:

可重构智能表面(RIS)正在成为一种有前途的技术,以提高5G和6 G网络的能效(EE)。受这种潜力的启发,在本文中,我们研究了RIS辅助的节能无线电接入网络(RAN)。特别是,我们结合RIS与睡眠控制技术,并开发了一个分层强化学习(HRL)算法的网络管理。在HRL中,元控制器决定异构网络中小型基站(SBS)的开/关状态,而子控制器可以改变SBS的发射功率电平以节省能量。仿真结果表明,RIS辅助的睡眠控制可以实现显着降低能耗,更高的吞吐量,和超过一倍的能源效率比无RIS条件。

英文摘要:

Reconfigurable intelligent surface (RIS) is emerging as a promising technology to boost the energy efficiency (EE) of 5G beyond and 6G networks. Inspired by this potential, in this paper, we investigate the RIS-assisted energy-efficient radio access networks (RAN). In particular, we combine RIS with sleep control techniques, and develop a hierarchical reinforcement learning (HRL) algorithm for network management. In HRL, the meta-controller decides the on/off status of the small base stations (SBSs) in heterogeneous networks, while the sub-controller can change the transmission power levels of SBSs to save energy. The simulations show that the RIS-assisted sleep control can achieve significantly lower energy consumption, higher throughput, and more than doubled energy efficiency than no-RIS conditions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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