Energy Saving of 5G Base Stations Based on Symbol Shutdown and Power Allocation

The rapid development of 5G technology leads to increasing energy consumption in base stations (BSs). For the vision of green and sustainable communications, we propose a scheme aimed at reducing BS energy consumption through symbol shutdown. This approach reduces BS energy consumption while ensuring the quality of service (QoS) for user equipments (UEs). Our scheme considers highly dynamic channel conditions and formulates a joint optimization problem, including BS shutdown and power allocation, constrained to the long-term average rate demand of all UEs. To address this problem, we design a Lyapunov method-based algorithm (LMBA) that transforms the mixed integer and dynamic optimization problem into a more tractable form and solves it using the Karush-Kuhn-Tucker conditions. Simulations indicate that our proposed strategy significantly reduces the BS energy consumption and the LMBA outperforms several benchmark algorithms.

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Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/icc52391.2025.11162039
Primary Topic
Telecommunications and Broadcasting Technologies
Type
article
Field-Weighted Citation Impact
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Energy Saving of 5G Base Stations Based on Symbol Shutdown and Power Allocation

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Telecommunications and Broadcasting Technologies
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Energy Saving of 5G Base Stations Based on Symbol Shutdown and Power Allocation

Huan Li, Haotong Cao, Meng Shi, Daosen Zhai, Yiyang Ni, Ruonan Zhang, Runliang Zhu
article en

Abstract

The rapid development of 5G technology leads to increasing energy consumption in base stations (BSs). For the vision of green and sustainable communications, we propose a scheme aimed at reducing BS energy consumption through symbol shutdown. This approach reduces BS energy consumption while ensuring the quality of service (QoS) for user equipments (UEs). Our scheme considers highly dynamic channel conditions and formulates a joint optimization problem, including BS shutdown and power allocation, constrained to the long-term average rate demand of all UEs. To address this problem, we design a Lyapunov method-based algorithm (LMBA) that transforms the mixed integer and dynamic optimization problem into a more tractable form and solves it using the Karush-Kuhn-Tucker conditions. Simulations indicate that our proposed strategy significantly reduces the BS energy consumption and the LMBA outperforms several benchmark algorithms.

Northwestern Polytechnical University (CN), Jiangsu Second Normal University (CN), Nanjing University of Posts and Telecommunications (CN)
Openalex Percentile: Top 61%
Telecommunications and Broadcasting Technologies
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