カーボンニュートラルに向けた5G基地局のエネルギー効率最適化と低炭素運用
Energy Efficiency Optimization and Low-Carbon Operation of 5G Base Stations Toward Carbon Neutrality (原題)
Zekai Yu
🤖 gxceed AI 要約
日本語
5G基地局の消費電力増大に対し、トラフィック負荷ベースの消費モデルを構築し、ハード・ソフト・再エネ・蓄電池・スマートグリッド連携を統合した低炭素運用枠組みを提案。シンボル停止や深いスリープ等の省電力手法とAI需給予測による動的制御を組み合わせ、全体消費電力を30〜45%削減可能と示す。通信網の脱炭素移行に資する実務的知見を提供する。
English
This paper models 5G base station energy use by traffic load and proposes an integrated low-carbon operation framework combining hardware/software optimization, renewables, storage, and smart-grid coordination. It designs an AI-based traffic prediction and dynamic energy-saving scheduling algorithm with layered intelligent management. Results indicate 30-45% overall energy reduction, supporting the low-carbon transition of communication networks.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
中国のデュアルカーボン目標を背景とした通信インフラの省エネ研究であり、日本ではNTTやKDDI等の通信各社がScope2削減・再エネ調達・省電力基地局を進める文脈と親和的。SSBJ開示でScope2・エネルギー効率の実務的裏付けとして参照価値がある。
In the global GX context
Amid global TCFD/ISSB-aligned Scope 2 and energy-transition disclosure, this work offers a concrete operational pathway for telecom infrastructure decarbonization. It adds empirical evidence on AI-driven energy scheduling and renewable integration relevant to CSRD and transition-planning discussions.
👥 読者別の含意
🔬研究者:通信インフラの負荷連動型エネルギー最適化とAI制御の実証枠組みを提供する。
🏢実務担当者:基地局の省電力・再エネ統合・蓄電池運用の設計指針として活用できる。
🏛政策担当者:通信分野の脱炭素政策・省エネ規制の技術的根拠として参照可能。
📄 Abstract（原文）
The large-scale rollout of 5G has sharply increased base station energy use, making it a major source of carbon emissions in communications. Against the background of China's dual-carbon targets, investigating low-carbon operation strategies for 5G base stations is of both theoretical significance and practical value. This paper analyzes the energy consumption composition and operational behavior of 5G base stations, establishes an energy consumption model based on traffic load, and proposes an integrated low-carbon operation framework that includes hardware energy optimization, software-driven consumption reduction, alternative energy adoption, and intelligent monitoring and control. On this basis, it investigates the principles and applicability of software-based energy-saving methods like symbol shutdown, channel shutdown, deep sleep, and carrier shutdown, as well as energy-side strategies such as coordination between base stations and smart grids, integration of solar and wind power, and optimized energy storage scheduling. Furthermore, an AI-based traffic prediction and dynamic energy saving scheduling algorithm is designed, together with a layered, coordinated intelligent management architecture. The results show that, by combining multidimensional technological coordination with intelligent management, 5G base stations can reduce overall energy consumption by 30%-45%, thereby providing technical support for the low-carbon transition of communication networks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.54254/2755-2721/2026.36872first seen 2026-09-17 04:48:16
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