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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.02250 |
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| _version_ | 1866914017345273856 |
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| author | Yu, Tao Huang, Kaixuan Wang, Tengsheng Li, Jihong Zhang, Shunqing Han, Shuangfeng Wang, Xiaoyun Zeng, Qunsong Huang, Kaibin Lau, Vincent K. N. |
| author_facet | Yu, Tao Huang, Kaixuan Wang, Tengsheng Li, Jihong Zhang, Shunqing Han, Shuangfeng Wang, Xiaoyun Zeng, Qunsong Huang, Kaibin Lau, Vincent K. N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02250 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TREE:Token-Responsive Energy Efficiency Framework For Green AI-Integrated 6G Networks Yu, Tao Huang, Kaixuan Wang, Tengsheng Li, Jihong Zhang, Shunqing Han, Shuangfeng Wang, Xiaoyun Zeng, Qunsong Huang, Kaibin Lau, Vincent K. N. Systems and Control 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. |
| title | TREE:Token-Responsive Energy Efficiency Framework For Green AI-Integrated 6G Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.02250 |