Full Text:  <1549>

CLC number: TN929.5

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2023-09-18

Cited: 0

Clicked: 2535

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Danning XU

https://orcid.org/0009-0008-9989-3342

Xiao LI

https://orcid.org/0000-0001-9660-9053

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Article info.
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Frontiers of Information Technology & Electronic Engineering 

Accepted manuscript available online (unedited version)


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


Author(s):  Danning XU, Yu HAN, Xiao LI, Jinghe WANG, Shi JIN

Affiliation(s):  National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Corresponding email(s):  220210746@seu.edu.cn, hanyu@seu.edu.cn, li_xiao@seu.edu.cn, wangjh@seu.edu.cn, jinshi@seu.edu.cn

Key Words:  Reconfigurable intelligent surface (RIS); Energy efficiency; Multi-cell communication system


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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.

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