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Rentao Gu
Researcher at Beijing University of Posts and Telecommunications
Publications - 145
Citations - 988
Rentao Gu is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Passive optical network & Linear network coding. The author has an hindex of 13, co-authored 127 publications receiving 691 citations. Previous affiliations of Rentao Gu include Peking University & Beijing University of Technology.
Papers
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Journal ArticleDOI
Baseband unit cloud interconnection enabled by flexible grid optical networks with software defined elasticity
Jiawei Zhang,Yuefeng Ji,Jie Zhang,Rentao Gu,Yongli Zhao,Siming Liu,Kun Xu,Mei Song,Han Li,Xinbo Wang +9 more
TL;DR: This article reports an experimental demonstration of elastic lightpath provision for cloud radio-over-flexible grid optical networks in a software-defined-networking-based testbed and proposes a software defined centralized control plane to coordinate heterogeneous resources from three domains: the BBU domain, radio domain, and optical domain.
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Machine learning for intelligent optical networks: A comprehensive survey
Rentao Gu,Zeyuan Yang,Yuefeng Ji +2 more
TL;DR: A detailed survey of existing applications of ML for intelligent optical networks is presented, classified in terms of their use cases, which are categorised into optical network control and resource management, and optical network monitoring and survivability.
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A Sequence-to-Sequence Air Quality Predictor Based on the n-Step Recurrent Prediction
TL;DR: The experimental results validated that the AAQP with n-step recurrent prediction had better performance than the related arts since the error accumulation was reduced, and the training time was significantly decreased compared with the original seq2seq attention model.
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Dynamic virtual network embedding over multilayer optical networks
TL;DR: An auxiliary graph model is proposed to address multilayer virtual network mapping in a dynamic traffic scenario and can be achieved by adjusting weights of the edges of the auxiliary graph, which depends on the purposes of the network operators.
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Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies
TL;DR: A “3S” architecture for AI-driven autonomous optical network is proposed, which can aid the optical networks operated in “self-aware” of network status, “ self-adaptive’ of network control, and "self-managed" of network operations.