Energy efficiency optimization for a RIS-assisted multi-cell communication system based on a practical RIS power consumption model

Danning XU , Yu HAN , Xiao LI , Jinghe WANG , Shi JIN

Front. Inform. Technol. Electron. Eng ›› 2023, Vol. 24 ›› Issue (12) : 1717 -1727.

PDF (914KB)
Front. Inform. Technol. Electron. Eng ›› 2023, Vol. 24 ›› Issue (12) : 1717 -1727. DOI: 10.1631/FITEE.2300136

Energy efficiency optimization for a RIS-assisted multi-cell communication system based on a practical RIS power consumption model

Author information +
History +
PDF (914KB)

Abstract

Reconfigurable intelligent surface (RIS) is widely accepted as a potential technology to assist in communication between base stations (BSs) and users in edge areas. We study the energy efficiency of a RIS-assisted multi-cell communication system with a realistic RIS power consumption model. With the goal of maximizing the energy efficiency of the system, we optimize the transmit beamforming vectors at the BS and the RIS phase shift matrix by a proposed alternative optimization algorithm. First, the transmit beamforming vector is optimized by solving the transformed weighted minimum mean square error (WMMSE) problem. Subsequently, to solve the inconvenience incurred by the discrete relationship between the RIS reflecting unit power consumption and its discrete phase shift, we use a continuous function to approximate their relationship. With this approximation, we can use the majorization minimization (MM) technique to optimize the continuous RIS phase shifts, and then quantize the obtained phase shifts to discrete ones. Simulation results demonstrate that the energy efficiency of the system is effectively optimized by the proposed algorithm.

Keywords

Reconfigurable intelligent surface (RIS) / Energy efficiency / Multi-cell communication system

Cite this article

Download citation ▾
Danning XU, Yu HAN, Xiao LI, Jinghe WANG, Shi JIN. Energy efficiency optimization for a RIS-assisted multi-cell communication system based on a practical RIS power consumption model. Front. Inform. Technol. Electron. Eng, 2023, 24(12): 1717-1727 DOI:10.1631/FITEE.2300136

登录浏览全文

4963

注册一个新账户 忘记密码

References

RIGHTS & PERMISSIONS

Zhejiang University Press

AI Summary AI Mindmap
PDF (914KB)

Supplementary files

FITEE-1717-23005-DNX_suppl_1

FITEE-1717-23005-DNX_suppl_2

472

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/