Mobile networks are starting to use Artificial Intelligence directly inside the Radio Access Network (RAN). Finland is playing a major role in this development, with recent work showing how AI-RAN can improve existing 5G networks while creating a practical technical path toward 6G. So, now let us look into how AI-Native RAN Is Moving 5G Networks Toward 6G in Finland along with Reliable LTE RF drive test tools in telecom & Cellular RF drive test equipment and Reliable Wireless Survey Software Tools & Wifi site survey software tools in detail.
The key change is where and how AI is used.
AI has been used in mobile networks for several years for traffic analysis, fault prediction and network optimization. AI-native RAN takes this further. AI processing becomes part of the radio architecture itself, allowing AI models to work with radio data and make decisions at very short time intervals.
This can influence functions such as beamforming, channel estimation, interference management, traffic prediction and radio resource allocation.
Why AI-RAN Matters for 6G
A conventional RAN depends heavily on predefined algorithms and network parameters. Engineers configure these parameters based on expected network behaviour, traffic conditions and RF requirements.
AI-RAN introduces another approach.
AI models can analyse changing radio conditions and adjust network behaviour using live network information. This becomes useful when a network has thousands of cells, different spectrum layers, changing traffic loads and large numbers of connected devices.
Recent AI-RAN work from Finland has already demonstrated more than 20% improvement in spectral efficiency from AI-driven radio functions. Further software development is targeting much higher efficiency improvements over the next few years.
This does not mean that every network will automatically achieve the same improvement. Actual performance depends on spectrum configuration, RF conditions, mobility, traffic distribution, cell-edge conditions, hardware and network architecture.
Still, even a smaller improvement in spectral efficiency can be significant for an operator because spectrum is limited and expensive.
Moving AI Closer to the Radio
One of the technical requirements for AI-RAN is accelerated computing.
Some radio decisions must happen within milliseconds. Sending data to a distant cloud platform, processing it and returning the result may introduce too much latency. AI processing therefore needs to move closer to the base station or network edge.
This is where GPU and other accelerated computing platforms are becoming part of RAN architecture.
AI models running close to the radio can process channel conditions, interference patterns and traffic information in near real time. The network can then adjust radio behaviour without waiting for a traditional offline optimization process.
This architecture is already being evaluated through laboratory tests and live network trials across several regions.
Existing 5G Infrastructure Can Continue
The move toward AI-RAN does not necessarily require operators to replace their complete 5G network.
Current development supports several deployment models.
Operators can add AI acceleration to existing baseband infrastructure, introduce dedicated AI-RAN computing nodes, or run cloud-native RAN software on commercial server infrastructure with accelerated computing.
The radio units and existing spectrum assets can continue to be used depending on the deployment architecture.
This is an important engineering point because 6G will not appear as a completely separate network overnight. Operators have already invested heavily in 4G, 5G and 5G-Advanced infrastructure.
The transition will happen gradually through software, computing and radio upgrades.
From 5G-Advanced to AI-Native 6G
5G-Advanced provides an immediate environment for testing many AI-RAN functions before 6G commercial networks arrive.
AI can already support better spectrum utilization, uplink performance, energy management and network automation. Future 6G systems are expected to take this further by making AI part of the network architecture from the beginning.
The RAN could therefore move from a system that engineers periodically optimize into one that continuously evaluates radio conditions and adjusts its operation.
This also changes network testing.
Testing an AI-native RAN will require engineers to measure more than RSRP, RSRQ, SINR, throughput and latency. They will need to understand how AI-controlled radio decisions affect mobility, interference, cell-edge performance, spectrum usage and user experience under different network conditions.
The work currently coming from Finland shows that the transition toward 6G is already starting at the RAN level. The first stage is not replacing 5G. It is making the existing RAN more software-driven, programmable and capable of using AI directly in radio operations.
That experience from live 5G and 5G-Advanced networks will help define how AI-native 6G networks are designed, deployed and tested.
About RantCell
RantCell is a mobile network testing and monitoring solution designed for testing 4G LTE, 5G and private mobile networks using Android smartphones. It helps network engineers collect and analyse RF and service-performance measurements such as RSRP, RSRQ, SINR, throughput, latency, network availability and coverage performance.
RantCell supports walk testing, drive testing, indoor testing and remote network monitoring without requiring traditional dedicated test equipment for many day-to-day measurement activities. Test results can be uploaded to the RantCell cloud platform for analysis, reporting and network performance assessment.
If you are evaluating 5G coverage, private networks or mobile network performance, request a RantCell demo and see how it can support your testing requirements. Also read similar articles from here.