There is a subtle evolution occurring in mobile networks. An evolution that most people don't think too deeply about — and even most of us don't think about what happens after we click on a link or launch an app. But somewhere between your phone and the cloud, a part of the network called the Radio Access Network (RAN) is being completely transformed. And why? Because it is starting to think for itself. A couple of years back, RAN was a fairly straightforward thing to deal with. You would have base stations, antennas, radios, and loads and loads of manual configurations. Engineers and planners would set parameters, build rules, and hope things worked. But as mobile networks became more and more congested, there came a need for more and more data especially with 5G and that was simply not good enough anymore. The System That Doesn't Wait for Instructions It is crucial that networks are no longer passive, waiting for someone to instruct them on what to do. Data is moving too quickly, there are too many devices, and deadlines and demand are simply too compelling to be ignored. Streaming and gaming, autonomous vehicles and deliveries, remote surgeries—when it is all high stakes and connected, it takes on an entirely new significance. In essence, rather than simply attempting to manage all of this complexity by hand, the industry turns toward intelligence. Not simply as some shorthand for AI either—intelligence. Learning systems. Real-time adaptation. The idea is simple, yet powerful, and that is to allow the RAN to self-adapt. Allow it to observe what is happening, and then respond faster than anyone could dream of, or even do in that day. Smarts at Every Level Smarts at Every Level This shift is not just taking place in one area, but is also partly being moved to the edge of the network itself and closer to where the data is being created, which means the decision is being made at the cell site and is not 'checking in' with some central source to decide what to do next. However, there does exist another layer of intelligence above the machine and the cell site that provides operational context and a complete understanding of patterns and issues across locations. One follows the road ahead of it, and the other looks over the horizon. Now, taken together, this becomes a network that reacts, plans, predicts, and is always ahead of the next thing. Less Waste, More Awareness Less Waste, More Awareness Energy has long been a problem for mobile networks. Radios and antennas consume a lot of energy, and they provide a persistent signal, even in massive MIMO scenarios. If things are not managed, you will end up with excessive demand and therefore consumption of energy. Up until recently, networks largely had no control over energy consumption. Now, imagine an intelligent system that knows when to take a pause. It is a late hour in an unpopulated neighborhood; the system shuts itself off, as demand is no longer present. The next day, demand comes back, and the system comes back online. This dynamic energy management is not simply good for energy expenses; it is essential to some degree. Think of it as a requirement to reach the next stage of future networks that will be larger and have more traffic. Making the Most of the Airwaves Making the Most of the Airwaves Spectrum supply is limited. It has always been limited, and it will continue to be limited as more and more services are moved to wireless technology and as the number of users and services grows. However, rather than treating spectrum as a limited resource, we will think about spectrum as an open and infinite resource, in which AI-based RAN can better utilize spectrum by optimizing it. In effect, the RAN will continuously optimize the network—changing channels, managing interference, and balancing loads appropriately. It will almost be like a network that optimizes continuously—not just once a month, or even once a day, but every minute of every hour of every day. Previously, the coordination of these types of changes was done by an engineer calling everyone involved to establish a running schedule. Then the engineer was left to guess how to configure everything and why everything would work the best. AI-based RAN will determine the best configuration in milliseconds and make the changes. Problems? Solved Before You Even Notice Problems? Solved Before You Even Notice This perspective has changed. In the past, a device would fail; someone would repair it. An alert would go off. A phone would ring. Afterwards, repairs would take place. When the average person thinks of network resiliency, they think of tower strength or backup systems that activate when something is wrong. That is part of the equation. It is also important to understand the speed or duration it takes for a system to realize an event has taken place — even if it is a small anomaly that has not yet escalated to great potential instability. We are beginning to see networks realize there has been an anomaly — a fading signal somewhere or an utter out-of-norm eruption in traffic — and they will manage to see the issue resolve itself. Quietly. Automatically. And most often, the person using their device will never know anything occurred or was even instigating instability. And that is by design. No more waiting for a catastrophe to unfold. One Size Doesn’t Fit All One Size Doesn’t Fit All Not every network looks the same anymore. In factories, ports, airports, and secure government environments, private RAN setups are growing fast. These places can’t afford latency. They can’t tolerate downtime. And they need networks that adapt on the fly. U.S. carriers are contending with various challenges. There are large urban areas which are generally network-stressed, from downtown to the suburbs, as well as rural areas that, when a wildfire or storm passes through, everything in service is gone in an instant. With this in mind, carriers are starting to think differently about network build-out. Open RAN is becoming more attractive—mainly due to its agility—and once they are ready, it is easier to deploy in that location. Countries have their own domestic responses to resiliency and integrity issues associated with the networks. Japan is discovering ways to keep their networks functional through an earthquake using AI to do so. Germany is conducting a series of trials of private networks for an industry that wants an umbilical issue of more reliability, integrity, and security. So maybe we all have our issues to address, but we are all going to the same place: build out the networks that are quicker to react and operate autonomously in time of need, when needed, with no one needing to act. That’s where intelligence really shines. These environments often change by the hour. A new machine goes online. A robot moves across a warehouse floor. The network needs to know — and it needs to respond without anyone telling it what to do. The transformation is possible with a RAN enhanced by AI. It facilitates a shift from a one-size-fits-all network architecture to one built around your own requirements. It Doesn’t Stop at 5G It Doesn’t Stop at 5G This goes beyond just the 5G network capabilities being maximized. This acts as a means to aid the evolution to the next generation. If 5G is built for speed and low latency, the next generation is built for speed and low latency but with a greater emphasis on consistency, autonomy, and smart coherency. The next generation of networks—whatever that ultimately looks like beyond 6G—will most likely be faster and lower latency than today's networks, but it may also directly support new capabilities for emergency event scenarios. For example, envision a drone that flies into a collapsed building not only at the direction of the remote pilot, but also with the network providing instructions to the drone. A different situation, such as a flood, would create similar scenarios. The network is able to determine which components of the network are seen as logical or significant and reorganize resources to help emergency response teams re-establish communication with a failed critical infrastructure. This type of spatial awareness will respond to an event without having a human or expert decision-maker, and likely represents one of the more unique features of 6G—in addition to faster responsiveness in emergencies, and the fact that the network has the ability to adapt on a real-time basis, like it is able to do today. That future is already beginning to emerge—not through flashy new advertisements or brand new product launches, but quietly happening in the background as RANs develop their own neurons! This story was distributed as a release by Sanya Kapoor under HackerNoon’s Business Blogging Program. This story was distributed as a release by Sanya Kapoor under HackerNoon’s Business Blogging Program.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

AI-Enhanced RAN: Building Smarter, Self-Adapting Wireless Networks

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Predictive Inventory: Real-Time Inventory Optimization with Multi-Cloud Data Warehousing

Tech Meets the Road: Patrick Dajos and Josh Norris Bring Decentralized Comms to the Niche Market

Building StyleGAN in PyTorch: From Theory to Code

"How do cutting-edge techniques in AI-enhanced scientometrics contribute to the field of research?"

The Noonification: Use This 7-Step McKinsey Framework to Solve Any Problem (1/10/2023)

The Noonification: A Taxonomy of Inclusiveness (1/11/2024)

Predictive Inventory: Real-Time Inventory Optimization with Multi-Cloud Data Warehousing

Tech Meets the Road: Patrick Dajos and Josh Norris Bring Decentralized Comms to the Niche Market

Building StyleGAN in PyTorch: From Theory to Code

"How do cutting-edge techniques in AI-enhanced scientometrics contribute to the field of research?"

The Noonification: Use This 7-Step McKinsey Framework to Solve Any Problem (1/10/2023)

The Noonification: A Taxonomy of Inclusiveness (1/11/2024)

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps