Graph Convolutional Network based Link State Prediction

Yeom, Sungwoong; Choi, Chul Yung; Kolekar, Shivani Sanjay; Kim, Kyungbaek

doi:10.23919/apnoms52696.2021.9562682

Cited by 3 publications

(1 citation statement)

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“…In another study, a GCN with Gated Recurrent Unit (GRU) cells was used to predict the state of links in a network [68]. In particular, the GCN was used to learn features of the network topology, while the GRU was used to model the temporal dependencies between link states.…”

Section: Novel Deep Learning Architecturesmentioning

confidence: 99%

B5GEMINI: AI-Driven Network Digital Twin

Mozó

Karamchandani

Gómez-Canaval

et al. 2022

Sensors

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Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML. First, we present the infrastructural and architectural components that support B5GEMINI. Next, we explore four paradigmatic applications where AI/ML can leverage B5GEMINI for building new AI-powered applications. In addition, we identify the main components of the AI ecosystem of B5GEMINI, outlining emerging research trends and identifying the open challenges that must be solved along the way. Finally, we present two relevant use cases in the application of NDTs with an extensive use of ML. The first use case lays in the cybersecurity domain and proposes the use of B5GEMINI to facilitate the design of ML-based attack detectors and the second addresses the design of energy efficient ML components and introduces the modular development of NDTs adopting the Digital Map concept as a novelty.

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Section: Novel Deep Learning Architecturesmentioning

confidence: 99%