It’s become almost a mantra in the industry: 5G will make automation a necessity, because of the large number of network devices that will be deployed to ferry the huge amounts of data that will inundate networks. Huawei, Ericsson, Nokia, and a host of others all have plans and programs that will help with that automation. And while 5G is still largely under development and hasn’t been rolled out on a large scale, it seems that MNOs that are doing so have not rushed to implement automation – even though every company involved in 5G holds by that mantra.
One reason for that is a perceived “lack of maturity” in automated RAN technology has been holding MNOs back from implementing 5G automation, according to a report by Rethink Research. One important factor holding companies back from fully embracing 5G technology, according to the study, is that “progress has been slower” in automating the network. That “immaturity in the technology” has “led to fears that operators would end up automating bad practice, or suffering trade-offs in the quality of their networks.”
Despite the need – and the attendant profits for companies that get there first – operators are right to hold off until automation is up to par. The sheer number of devices needed to run a 5G network – small cells, picocells, mini-macro base stations, complex antenna arrays, etc… will require an automated eye to keep things running. Repairing and maintaining all those systems is going to be a challenge, to say the least.
Along with the maintenance use case, there is a deployment one as well. Automation will enable MNOs to more easily deploy resources as needed, optimizing configurations as needed in order to optimize customer experience. When 100,000 fans are streaming video live from the Super Bowl, communication service providers (CSPs) can ensure that network capacity in the area of the stadium expands and is robust enough to ensure a perfectly smooth experience for everyone.
It all sounds great, but the question of how to get there from where we are now has plagued the industry ever since large-scale deployment of 5G has been feasible. Keeping up with all the equipment and keeping track of all the deployment scenarios is going to be impossible, I believe – unless artificial intelligence is part of the solution.
AI is largely based on machine learning – where algorithms compare thousands of data points in order to determine maximum efficiency or operating parameters – fits hand in glove with RAN automation.
Machine learning AI-based systems can sweep equipment of all types – all the different components of a 5G network “in the wild” – and examine their performance, determining if it matches the ideal parameters that CSPs require and expect. The AI system learns how a network is supposed to operate in the multifaceted scenarios it is subjected to, and builds a model for each piece of equipment, each deployment algorithm. If something is not performing as it should, or appears to be headed in that direction (ie, a piece of equipment cannot handle X amount of connections when Y conditions prevail), the AI system will be able to determine that in advance, and warn MNOs of the problem so that systems can be repaired/upgraded/revised to ensure maximum efficiency at all times.
The same goes for deployment scenarios. The AI system “gets to know” all the components in a network and how they work together – and is able to project how the entire network and all its components will perform under normal and stressful circumstances. MNOs can tap into that intelligence at any time and get a clear idea of what to expect when faced with a service crunch – and prepare as needed to avoid that crunch. Once the equipment and deployment issues are clear, MNOs can begin the work of automating their networks, confident that things will happen when they are supposed to, and equipment will behave as needed.
AI and machine learning is the answer to the conundrum of how CSPs can finally move forward with 5G – remaining confident that their systems will work, and that customers will remain satisfied.
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