Automation Challenges in 5G

5G network automation allows operators to develop machine-driven approaches that address their business objectives. On the other hand, it’s a way of delivering on-demand services customized to meet every customer’s needs. There is a need to get the balance correctly. Additionally, balance is also crucial in the rollout of the 5G network from the infrastructure point of view. Operators require tools that can operate on physical and virtual networks and probably on hybrid architectures that are combinations of the two to face automation challenges in 5G.

Autonomous networks are considered an ambition for communication service providers (CSPs), but it is now clear that these networks are mandatory to remain afloat. CSPs and suppliers during TM Forum established four 5G use scenarios that require machine learning (ML) and artificial intelligence (AI) to support network automation.

Next is finding repetitive patterns in the use cases and coming up with ways to deliver them. But it is not about delivery alone but with network and cloud resources that configure, optimize, organize, heal and evolve without intervention.

CSPs must commit to fully automate the network and operation resources since meeting the latency and availability requirements of 5G apps such as autonomous cars might be impossible if manual processes rely on provisioning, managing, and assuring the case today. If telcos fail to entirely revamp the network to optimize 5G capabilities such as network-functions and software-defined networking, someone else will deliver the services.

Automation Increases Complexity

The overall objective of automation is to simplify, but unfortunately, it significantly increases complexity. CSPs could take up to several months to reach agreements with large enterprise clients for VPN services, but 5G applications push for real-time scale on demand for network capabilities.

Although it takes a short time, below 15 minutes, to connect numerous websites across the world with high-speed access, virtualized infrastructure, and superactive cloud data centers, the process won’t require any human intervention.

Use of Autonomous Vehicles to Illustrate the Complexity

The autonomous vehicle gives a perfect illustration of the automation challenge in 5G. The team participating in the TM Forum several 5G uses scenarios that need resource management automation, including connectivity as a service, remote surgery, autonomous vehicles, and energy management. From the list, the team settled on four use cases to approve the concept.

1. Connected vehicles

It includes various scenarios such as autonomous driving and delivery of online gaming to a passenger.

2. Broadcast media

It includes representing an instance of pop-up networks in covering sports or news events.

3. Smart cities

It features cases such as the Internet of Things sensor networks for real-time management of traffic, energy, and guaranteeing emergency services.

4. Smart manufacturing

It is a scenario that includes quick design changes, tracking of employee safety, and Internet of Thing sensors.

Connected Vehicles

Connected vehicles have varied aspects, including machine-machine, high availability, and low latency, perfectly illustrating the need for automation and complexity. It is the reason behind the use of autonomous vehicles in most instances.

To understand the complexity, figure out the following situation.

• A vehicle with an eSIM moving along a 10-mile road at 60 mph
• The latency requirement for the eSIM session to move between cell towers is 30 microseconds
• There are millimeter-wave cell towers after every 400 meters along the road.
• There are ten computers in the car that communicates through the Internet of Thing applications.
• The apps run on a similar edge infrastructure and communicate with the vehicle manufacturer, an insurer, a power company, and a government authority such as the EPA or the Department of Transportation.
• A passenger is playing an online game in the backseat.
• The vehicle is self-driving and communicates via vehicle to vehicle.

The above situation presents different workloads whose priorities differ and keep shifting across the network as the vehicle moves.

There must be a strategy to ensure there is no collision between the converged infrastructure workloads to run. Although it might not be possible to do it today, there is a need for a connected packet network, edge cloud infrastructure, and artificial intelligence application in the future to realize autonomous resource management.

The Bottom Line

The above scenario depicts a single vehicle in a 10-mile stretch. Suppose you figure out the situation in a city where millions of self-driving cars move in different directions. In that case, you can imagine the complexity that such a scenario would bring. Therefore, autonomous resource management is vital.

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