The how and why of AI: enabling the intelligent network
We might feel we’re in control of our everyday lives… Far from it! Today, whether we realize it or not, artificial intelligence (AI) is having an impact on us and influencing our behavior. Take the chat bot we interact when shopping online, or the personalized recommendations of streaming services… We even have smartphone cameras that register our credit cards… All things considered, AI actually serves us rather well, making life easier and often more secure. Now the radio access space is also about to benefit in a big way.
It’s easy to throw in a buzz word like AI these days to get some attention. But that’s not my only intention with this piece. My aim is to offer some valuable insights into how AI can make a real difference in the world of radio access networks (RANs), looking at the way it works as well as the multiple benefits it brings.
Let me elaborate…
From a technology perspective, AI in the RAN is about letting the hardware adjust its decision-making capability and allowing it to learn from patterns. Learning can improve the baseband functionality, which otherwise acts based on hard-coded settings. Of course, the pattern observations and execution of AI and machine learning (ML) require a certain amount of computing capacity. They also depend on software algorithms for decision making, as well as one or several data sources to use for pattern analysis.
Typical traffic patterns
While device types vary to some extent, 70 percent of all those used today are smartphones*. Their ability to differentiate depends on the relative level of their capabilities. Smartphones range from feature phones that support voice and text only, to full-on high-end 5G-capable devices.
Within the high-end smartphone segment, traffic behavior patterns for data services mostly shift between bursty and continuous. But on the networks side, the necessary capacity should always be available, regardless of what kind of app you are using at any given moment – whether it’s chat, video streaming or gaming, for instance.
Sourcing data dynamically
Smartphones perform measurements all the time, without most users being aware of it. These measurements are necessary to manage the radio link and the mobility, and to control how much output power each device needs to use. The network collects the measurement data in order to decide on the best connection for the device. Smartphones also carry key information about their network capabilities, which they conveniently report to the network. For instance, not all smartphones have 5G, but for those that do, the node can prepare a tailored network connection for each particular user.
Neighbor cells also report to each other on the status of capabilities, connected users and current load. This information can also be taken into consideration.
The significance of hardware
Ultimately, the scale of the benefits that AI can provide is determined by the hardware in place and the location of the boards. The hardware components of a mobile network today have to meet huge requirements in terms of managing the mobility of thousands of users – in each cell. Not only that: they must also make sure that no connections are dropped and that the service is responding at all times.
Of course, routing and more central functions are rather executed from the core network components. So the node base stations do not have to carry full responsibility for the effectiveness of the entire network on their shoulders. But real-time mobility functions are located at the edge of the network, on the node.
Today’s node often houses GSM, WCDMA, LTE and NR on a single site – not always on one baseband, but such installations are soon to become commonplace as well.
Optimized algorithms, software-integrated machine learning
Applying ML to software functionalities boosts the strength of the network significantly, since many network functions can benefit from the same algorithms. But the advantage of this comes at a cost, with some computing power being seized by the AI technology.
An Ericsson baseband will however run ML in parallel with regular node traffic without reducing the capacity of the baseband. That’s because our AI engineers have optimized the algorithms so that they can analyze huge amounts of data in real time, enabling instant traffic prediction. All this is facilitated by Ericsson’s many-core architecture, which is the software platform design of choice that all RAN Compute products are based on.
The reality is, service providers expect full-steam performance from their legacy products, even when new network capabilities are added – and Ericsson is aware of this. Service providers also like to minimize opex – and they incur significant operational costs when site visits need to be carried out. Ericsson is aware of this as well, which is why our ML features are integrated with our software releases, which can be applied on a remote basis without the need for any site visits at all.
Bring on the benefits of AI
We have reaped the benefits of AI in many areas of our lives – from movie offerings being handed to us on a plate, to the voice and face recognition apps in our smartphones to the optical scanning features of our credit cards. You can look at your phone with your eyes wide open and unlock it, you can register your credit card details using the camera in your device… In all such cases, AI simplifies the use of our devices, automating the steps that would otherwise have to be carried out in a repetitive, manual fashion. Imagine the hassle!
On the mobile network side, the use of AI is similar but not quite the same. While the initial use cases have been about automation, they are also about improving network coverage and the user experience by anticipating the needs of devices.
One practical example of this is that the measurements that smartphones carry out – which were mentioned previously – can be reduced significantly. By shortlisting the top neighbor cells at every node, the device will get an equally shortened to-do list of frequencies to listen to. This means that instead of numerous background measurements being performed by the device, battery power is conserved to do other fun stuff with.
For the service provider, one main benefit of implementing AI will be the reduction in opex, as fewer node configurations need to be added manually. But even more importantly, their spectrum assets can be used more efficiency, and spectrum is a valuable resource that they tend to have to pay for dearly.
All in all, AI for radio access networks is a sound investment. Ericsson’s software will improve coverage and spectrum use, and boost throughput. Then service providers can sit back, relax and let the machines do the work.
Ericsson Mobility Report, June 2019