Genet

Xia, Zhengxu; Zhou, Yu; Yan, Francis Y.; Jiang, Junchen

doi:10.1145/3544216.3544243

Cited by 17 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [8] decompose networking systems into local systems and global systems, and use the decision tree method to interpret a single RL agent's decision-making process in the local system. In [9], the curriculum learning is introduced to improve the generalization of RL. They use rule-based algorithms to generate training curricula.…”

Section: Design Principlesmentioning

confidence: 99%

Accurate Interpretation of the Online Learning Model for 6G-Enabled Internet of Things

Huang

Li²,

Tian³

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

The 6G Internet poses intense demands for intelligent and customized designs to cope with the surging network scale, dynamically time-varying environments, diverse user requirements, and complicated manual configuration. However, traditional rule-based solutions heavily rely on human efforts and expertise, while data-driven intelligent algorithms still lack interpretability and generalization. In this paper, we propose the AIGI (AI-Generated Internet), a novel intention-driven design paradigm for the 6G Internet, which allows operators to quickly generate a variety of customized network solutions and achieve expert-free problem optimization. Driven by the diffusion modelbased learning approach, AIGI has great potential to learn the reward-maximizing trajectories, automatically satisfy multiple constraints, adapt to different objectives and scenarios, or even intelligently create novel designs and mechanisms unseen in existing network environments. Finally, we conduct a use case to demonstrate that AIGI can effectively guide the design of transmit power allocation in digital twin-based 6G networks.

show abstract

Section: Design Principlesmentioning

confidence: 99%