5G and AI are two of the world’s most disruptive technologies to look into in 2022. While each revolutionizes sectors and creates new experiences on its own, the combination of 5G and AI will be really disruptive. On-device computing, the edge cloud, and 5G work together to form a ubiquitous connectivity fabric of smart devices and services. This point of convergence is critical to our concept of the intelligent wireless edge.
The influence of 5G
The commercial deployment of 5G has begun. But, to put it another way, 5G isn’t just another G. It’s a total ecosystem shift in how networks are managed and administered, as well as how apps function on them.
In 5G, there are three primary use case categories:
- EMBB stands for Enhanced Mobile Broadband.
- The millimeter-wave spectrum is used for fixed wireless access.
- For our residential internet, the most frequent usage is fiber, while the most common use is cable.
Massive machine-type communication, or MTC, is another rising use case category.
What role does AI play in 5G?
AI is not just nice to have when it comes to 5G networks, but a need to deal with the technology’s immense complexity. AI, along with the data and automation capabilities that come with it, helps sustain a varied ecology of growing networks in ways that humans alone can’t.
Because of its potential to alter sectors, 5G has great expectations. 5G provides excellent performance, low latency, throughput, and availability, which service providers anticipate. As a result, the ability to run 5G networks will improve quickly. In fact, high-level operational capabilities such as zero-touch and self-healing networks are currently being developed to meet this expanding need.
AI is improving 5G in both the network and the device
AI will be integrated into both the 5G network and the device, resulting in more efficient wireless connections, better battery life, and improved user experiences.. AI is a vital tool, and the key to using it to enhance wireless is to concentrate on critical wireless difficulties that are difficult to tackle using traditional approaches and match well with machine learning. To determine where to leverage AI’s capabilities, you need deep wireless domain knowledge.
Much of the discussion in the telecom industry has been on how AI would improve the 5G network. By recognizing unexpected spectrum utilization, AI might detect abnormalities in network traffic, such as flooding or impersonation. AI will undoubtedly significantly influence numerous critical aspects of 5G network management, including improved service quality, easier deployment, increased network efficiency, and increased network security.
Important issues to consider
Data, particularly how to structure network operations to be data-centric and data-driven, is one of the most challenging difficulties facing network evolution. The data pieces of a 5G network, for example, are widely dispersed. It’s available in a variety of forms, sizes, and volumes. So, how can this information be managed effectively? Data, after all, is what makes skills like machine learning and sophisticated analytics possible. Researchers won’t be able to run future networks without it.
For starters, service providers must have a well-defined and implemented data-driven strategy that guides how data should be managed across operations from ingestion to final decision making.
Second, precise decisions about where and how data is handled must be established so that AI logic can make quick judgments. For example, data might be sent to a centralized cloud location for AI inference processing, but this could result in high transfer costs and extra delays – especially in real-time use scenarios where decisions must be taken in a split second. Instead, AI inference might be brought closer to the data source, resulting in a shorter, more efficient pipeline.
Another crucial issue is maintaining data quality and lineage from beginning to finish so that choices may be taken based on reliable and high-quality data. It’s pointless to rely on AI reasoning if the data is skewed.
Finally, with 5G and AI implementation, organizational transformations incompetency, technological development, and future-proofing employees’ abilities might all be obstacles.
How to Address 5G and AI Adoption Issues
Ericsson shifted its strategy from reactionary to proactive and predictive to meet these new difficulties, which is the foundation of our AI modeling. We’re also upskilling our workers so they can view the network from end to end, in addition to our data-driven strategy. The Ericsson Operations Engine is the culmination of this process.
Researchers also concentrate on data analytics, competence development, and 5G technologies and the creation of particular use case specialists to meet emerging market demands. We need to comprehend the entire ecosystem of these developing use cases – not only the technology but also the many platforms and tools that may aid in the smoother and more automated running of network operations.
Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications