5G bearer network operation is difficult, but machine learning technology can solve it

5G commercialization is getting closer and closer, and there is not much time left for operators to build 5G bearer networks. According to forecasts, the average user's monthly data usage in the 5G era will reach about 45GB. The daily traffic in industries such as video, IoT, and live broadcasting will reach the PB level. Faced with complex applications and massive traffic, machine learning has the ability to drive automation through insights and achieve process automation, which can help operators build 5G bearer networks.

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5G bearer network new architecture needs fresh blood

The three major operators have all scheduled 5G commercial use in 2020. In this year or so, operators need to work overtime to build 5G bearer networks. 4K video, VR, online games, etc. will make users consume more data in the 5G era. In addition, the explosion of the Internet of Things will increase the number of access devices by hundreds of times.

Therefore, building a new network architecture has always been the choice of the three major operators. In addition to network function virtualization (NFV), software-defined networking (SDN), and practices of large Internet companies (CORD, SDL, DevOps), machine learning is ushering in the intelligent era of 5G bearer networks.

Machine learning takes care of it

For operators, why should we introduce machine learning into the bearer network? It is because machine learning has the characteristics of insight-driven automation, which can form a closed loop between purpose and result. For example, when a user visits a website to watch a video, machine learning will process the information and provide feedback through programmable operator-level SDN control, network provisioning and optimization, and service automation.

In addition, machine learning can also realize process automation functions. For example, machine learning functions can deeply analyze network traffic, provide network optimization and in-depth operation capabilities, and vigorously ensure the security of modern IP networks. Through application behavior, network analysis can be carried out and the network can be dynamically adjusted in real time.

No hardware investment required, trustworthy

Machine learning is embedded in the 5G bearer network and does not require any hardware. It is a pure software solution. In the past, the collection of information and data in the network was based on the hardware solution of network probes. This solution is expensive and will affect the stability of the network. The machine learning solution does not need to replace or embed hardware. As long as it is run in the bearer network, it can perform real-time analysis.

So far, 150 operators and enterprise users around the world have embedded machine learning solutions into their network architectures. After speed and security tests, it has been shown that machine learning solutions can effectively solve the problems of 5G bearer networks.

Conclusion:

At present, there are no drawbacks to the use of machine learning in 5G bearer networks. The three major operators still have more than a year to adjust their 5G bearer networks. In the new network architecture, new technologies such as SDN, NFV, AI, and machine learning are constantly introduced to solve the problem of 5G network's ability to process information. The addition of machine learning technology is just one example. No matter what technology is used, the ultimate goal is to improve users' 5G network experience.