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Applying machine learning in network topology optimization
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Authors: Zhouwei Gang, Qianyin Rao, Lin Guo, Lin Xi, Zezhong Feng, Qian Deng
Status: Final
Date of publication: 20 August 2021
Published in: ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4 - AI and machine learning solutions in 5G and future networks, Pages 91-99
Article DOI : https://doi.org/10.52953/FKID2877
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 | Abstract:
Nowadays, telecommunications have become an indispensable part of our life, 5G technology brings better network speeds, helps the AR and VR industry, and connects everything. It will deeply change our society. Transmission is the vessel of telecommunications. While the vessel is not so healthy, some of them are overloaded, meanwhile, others still have lots of capacity. It not only affects the customer experience, but also affects the development of communication services because of a resources problem. A transmission network is composed of transmission nodes and links. So that the possible topology numbers equal to node number multiplied by number of links means it is impossible for humans to optimize. We use Al instead of humans for topology optimization. The AI optimization solution uses an ITU Machine Learning (ML) standard, Breadth-First Search (BFS) greedy algorithm and other mainstream algorithms to solve the problem. It saves a lot of money and human resources, and also hugely improves traffic absorption capacity. The author comes from the team named "No Boundaries". The team attend ITU AI/ML in 5G Challenge and won the Gold champions (1st place). |
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Keywords: 5G, artificial intelligence (Al), data handling, intelligence level, machine learning (ML)
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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