Page 14 - 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

AI-based network topology optimization system

Pages 81–90
Han Zengfu, Kong Jiankun, Wang Zhiguo, Zhang Yiwei, Liu Ke, Pan Liang, Li Sicong, Wu Desheng

Existing network topology planning does not fully consider the increasing network traffic and problem
of uneven link capacity utilization, resulting in lower resource utilization and unnecessary investments
in network construction. The AI-based network topology optimization system introduced in this paper
builds a Long Short-Term Memory (LSTM) model for time series traffic forecasting, which uses
NetworkX, a Python library, for graph analysis, dynamically optimizes the network topology by edge
deletion or addition based on traffic over nodes, and ensures network load balancing when node traffic
increases, mainly introducing the LSTM forecasting model building process, parameter optimization
strategy, and network topology optimization in some detail. As it effectively enhances resource
utilization, this system is vital to the optimization of complex network topology. The end of this paper
looks forward to the future development of artificial intelligence, and suggests the possibility of how to
cooperate with operator networks and how to establish cross-border ecological development.
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Applying machine learning in network topology optimization

Pages 91–99

Zhouwei Gang, Qianyin Rao, Lin Guo, Lin Xi, Zezhong Feng, Qian Deng
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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