This versatility requires more than powerful hardware. It requires intelligent networks, capable of learning, organizing, optimizing, and adapting themselves. And this is where artificial intelligence (AI) and automation come into play.
From mobile networks to learning systems
Traditionally, mobile networks were fairly static: once configured, they operated according to predefined parameters and depended on human intervention. But the complexity of today's networks makes this approach increasingly inefficient, especially when operators work with multiple providers and are forced to constantly expand their infrastructure.
Automation represents a paradigm shift that enables routine tasks, such as network load distribution, configuration of new cells, and fault detection, to be performed automatically and in real time. AI-based systems go a step further: they are capable of analyzing large volumes of operational data, detecting patterns, and generating specific proposals or measures to optimize the network based on this analysis.
Why automation isn't enough and AI makes the difference
An automated network is capable of executing predefined processes. However, the real-world environment of a network is dynamic, as user behavior changes depending on the time of day and potential events or emergencies. Technical failures also do not follow predictable patterns.
This is where AI unleashes its full potential: algorithms can detect changes in time and proactively adapt the network. For example, they help to efficiently utilize frequencies, predict failures, or guarantee specific quality requirements (such as low latency for video games or high reliability for emergency services).
These systems learn continuously, either through data gathered from real-world operation or through simulations. Over time, increasingly autonomous networks are created that only require human intervention in exceptional cases.
Examples of use: what is already a reality today
One example is network slicing, which allows the creation of virtual networks ("segments") on the same physical infrastructure, each with its own performance characteristics. An autonomous vehicle, for example, could use a network with ultra-low latency, while a home automation device with low data volume would use a network optimized for energy efficiency. AI is responsible for allocating resources flexibly and efficiently and preventing interference between the different use cases.
Another example is energy optimization: many mobile networks, especially in rural areas, don't operate at full capacity all the time. AI can help temporarily disconnect certain components without the user noticing. This saves energy and reduces operating costs without affecting network quality.
How are network operators preparing for the future?
Currently, many operators are in a transition phase, progressively transforming their existing systems into smarter networks. To achieve this, they are using modular software solutions that integrate existing infrastructures while also being compatible with new standards such as O-RAN (Open Radio Access Network).
It is essential to adopt a holistic approach, as AI and automation can only reach their full potential when they operate across all areas of the network, from the radio access network (RAN) to the core network, including the transport network. Only then can they exchange information, coordinate decisions, and leverage synergies.
Conclusion: AI is not an accessory, but the key to the next generation of networks
The evolution of 5G and the future transition to 6G will not be possible without AI and automation. We're not talking about optional add-ons, but fundamental elements for a network that is scalable, flexible, and efficient.
It's not just about efficiency, but also about innovation: new services, business models, and user experiences will emerge precisely where networks are smarter. Those who are investing in learning systems today are laying the foundation for the communication of tomorrow.
Article provided by Nokia
