To know these two technologies inside out we need to consider their possibilities as well as the potential risks and pitfalls. Telecom giants are investing billions into them. Despite the possibilities of adverse consequences, organizations are taking the step to unify these two technologies. This post is going to investigate the intersection and future of Artificial Intelligence and 5G.
How does 5G work?
The network region can be divided into smaller geographical blocks called cells. 5G networks are inevitably digital cellular networks of these cells. The spectrum that the network uses to communicate can be classified into 3 types namely Low-band, Mid-band, and High-band spectrum.
The Low-band spectrum, characteristic of the 4G mobile communications standard (LTE), uses sub 1 GHz spectrum. The main advantages of the Low-band spectrum are its very high coverage and penetration. The drawback of poor data transfer speed (~100 Mbps) though makes it inefficient for fast communication.
The Mid-band spectrum tries to bridge the gap between the Low-band and the High-band spectrum. Mid-band provides higher coverage and lower latency than Low-band. The issue of low penetrative power plagues the Mid-band spectrum.
The High-band spectrum is truly what you can consider being 5G. Often stylized as mmWave, High-band provides unrivaled speed (often reaching 10 Gbps). Thanks to such a high transfer rate, the latency associated with the network become pretty low making it possible to use it for real-time communication. The main disadvantage of the High-band is that it has a limited coverage area and penetrative power.
AI and 5G
Robotics is one of the many sectors that are in the position to be benefitted from the merger of AI of 5G. This type of unification would enable robotics to produce intelligent robots to act within a broader range of environments. There is always a high chance of them being misused for defense purposes and monitoring the masses. But on the other hand, they have a high potential to be useful in hospitals and infirmaries.
It can be quite challenging to unify AI in the telecom sector. Using predictive analytics and Machine Learning tricks in the 5G network for improving the overall utility in the radio spectrum seems like a good direction. Autonomous (self-driving) vehicles, automating/managing/optimizing public transportation systems, automation in the time-sensitive sectors, and remote healthcare services are some of the many use cases of these technologies.
How 5G will help AI?
Vendors who deal with AI prominently have started considering the significance of 5G. Tech giant like NVIDIA is making a significant investment in various 5G-based services – service, for example, that help enhance the mobility, and interact with edge environments and Internet of Things (IoT). 5G is expected to deliver value to the AI sector in some of the following ways.
Massive device concurrency
Concurrency plays a huge role in scaling out your product. In order to take advantage of the multi-access edge computing system, you need to connect to the neighboring mobiles, sensors, and any other 5G enabled devices.
It’s evident that 5G can support up to 1 million concurrent edge devices in a square kilometer of area. You barely ever even come close to that threshold for sparsely populated countryside or suburban regions. But for the densely packed metropolitan regions where the population density can easily cross the 10000/sq. km mark (sometimes reaching 25000+/sq. km) it becomes relevant.
It’s no secret that the latency in 5G connections is much lower compared to the 3G or even 4G connections. The principal advantage of using 5G is its unparalleled speed. The average latency, for example, for 4G is nearly 50 milliseconds whereas for 5G it becomes as low as 1 millisecond.
Ultra-high-speed upload and download speed are characteristic traits of 5G. The speeds can go as high as 20 gigabits (compare that to the 5-10 megabits of 4G – almost 1000 times slower).
On-chip AI systems
5G helps converge digital facilities like cellular technology with Wi-Fi interfaces and wireless long-term Evolution. 5G can help roam between indoor and wide-area environments seamlessly.
Proper implementation of this technology will eventually lead to the convergence of disparate radio channels. It can also converge the network interfaces down to a single chip which is especially useful for maintaining connections across multiple radio access technologies.
How AI will help 5G?
AI is a key factor that needs to be considered to ensure that 5G networks, and the communication system as a whole, can support AI along with other application workloads. A recent study conducted by Ericsson stresses that many organizations are well on their way to reap the benefits of AI. The report claims 70% believe that AI has the potential to help in network planning while 64% states that the efforts should be focused on performance management.
Distributed AI applications
5G is the future of the next generation of distributed AI apps. In order to make the 5G network more stable, secure, and manageable AI is going to play an important role. Embedding AI and Machine Learning models effectively are the biggest challenge.
These two would ensure QoS (Quality of Service) assurance, application-level traffic routing, performance analysis, and many other operational tasks. In essence, they are going to help solve these issues and make scaling and prediction more efficient and effective.
AIOps, as it is known as a whole, is going to be the future of faster, more reliable, and more efficient connections. AIOps would come in handy in sorting out many network management issues in many different sectors including multi-cloud management suites and network virtualization.
AIOps boosts one aspect of the 5G infrastructure known as network slicing. Network slicing enables networks to run several virtual networks over a single physical connection. This virtualization of many aspects of the network help in a multitude of different ways.
For example, leveraging the virtualized resource provisioning using network slicing helps support predictive and dynamic delivery to diverse customer types and edge-device classes.