Page 15 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4

Analysis on route information failure in IP core networks by NFV-based test
environment

Pages 101–112

Xia Fei, Aerman Tuerxun, Jiaxing Lu, Ping Du, Akihiro Nakao
Stable and high‑quality Internet connectivity is mandatory for 5G mobile networks. However, the
pandemic of COVID‑19 has forced global and large‑scale staying at home and telecommuting in many
countries. The increasing traffic has induced more pressure on networks, devices and cloud data centers.
It becomes an essential task for network opera‑tors to enable their ability to automatically and rapidly
detect network and device failures. We propose a highly practical method based on highly practical
technology. Our method has a high generalization ability that can efficiently extract features from
large‑scale unstructured data and ensure high accuracy prediction. First, 997 useful features are
extracted from 28GB‑per‑day network logs. Then, a differential approach is employed to preprocess
the extracted features so as to highlight the differences between normal and abnormal states. Third,
those features are refined based on the feature importance we calculated. According to our experiment,
the proposed feature extraction and refinement method can reduce computation without degrading the
performance. Among the five types of failures, we achieve a 100% recall rate in four types and the rest
can also reach 71%. Overall, the total average prediction accuracy of the proposed method is 94%.

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Simulation of machine learning-based 6G systems in virtual worlds

Pages 113–123
Ailton Oliveira, Felipe Bastos, Isabela Trindade, Walter Frazão, Arthur Nascimento, Diego Gomes,
Francisco Müller, Aldebaro Klautau
Digital representations of the real world are being used in many applications, such as augmented reality.
6G systems will not only support use cases that rely on virtual worlds but also benefit from their rich
contextual information to improve performance and reduce communication overhead. This paper
focuses on the simulation of 6G systems that rely on a 3D representation of the environment, as captured
by cameras and other sensors. We present new strategies for obtaining paired MIMO channels and
multimodal data. We also discuss trade‑offs between speed and accuracy when generating channels via
ray tracing. We finally provide beam selection simulation results to assess the proposed methodology.
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