TREE:Token-Responsive Energy Efficiency Framework For Green AI-Integrated 6G Networks

Tao Yu; Kaixuan Huang; Tengsheng Wang; Jihong Li; Shunqing Zhang; Shuangfeng Han; Xiaoyun Wang; Qunsong Zeng; Kaibin Huang; Vincent K. N. Lau

arXiv · 2509.02250

TREE:Token-Responsive Energy Efficiency Framework For Green AI-Integrated 6G Networks

Tao Yu·Kaixuan Huang·Tengsheng Wang·Jihong Li·Shunqing Zhang·Shuangfeng Han·Xiaoyun Wang·Qunsong Zeng·Kaibin Huang·Vincent K. N. Lau

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Abstract

As wireless networks evolve toward AI-integrated intelligence, conventional energy-efficiency metrics fail to capture the value of AI tasks. In this paper, we propose a novel EE metric called Token-Responsive Energy Efficiency (TREE), which incorporates the token throughput of large models as network utility carriers into the system utility. Based on this metric, we analyze the design principles of AI-integrated 6G networks from the perspective of three critical AI elements, namely computing power, model and data. Case studies validate TREE's unique capability to expose energy-service asymmetries in hybrid traffic scenarios where conventional metrics prove inadequate. Although it is impossible to determine every design detail of AI-integrated 6G network at current time, we believe that the proposed TREE based framework will help the network operators to quantify the operating energy cost of AI services and continue to evolve towards sustainable 6G networks.

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BibTeX RIS

Tao Yu, Kaixuan Huang, Tengsheng Wang, Jihong Li, Shunqing Zhang, Shuangfeng Han, Xiaoyun Wang, Qunsong Zeng, Kaibin Huang, Vincent K. N. Lau. 2025-09-02. TREE:Token-Responsive Energy Efficiency Framework For Green AI-Integrated 6G Networks. https://arxiv.org/abs/2509.02250

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