How Machine Learning Is Best For 5G Network Performance
More equipment, more advanced technology, more coverage, and capabilities. There’s a clear line of progress as each new generation of wireless technology comes into use, from 3G via LTE and innovative 4G, and today 5G, the upcoming big thing in wireless.
The transformation from 3G to 4G within the past few years saw the deployment of more cells in different form factors, as well as new technology, such as business aggregation and full-packet changing, which makes 4G networks a lot more complex than 3G. More complicated but still manageable.
With the move to 5G, nevertheless, that manageability will really be put to the test. 5G networks will observe the deployment of an order of size with cells (interlaced into the existing fabric of 4G LTE macro-cells) and antennas (both passive and active ones that utilize many frequencies), with much more innovative technologies such as vRAN, which allows for the partial or even complete virtualization of networks, allowing quicker and cheaper updates and innovations in support.
With a more complex system comes more complex issues — in the sense that tracking down the origin of a service outage or other issue will be more difficult due to the system’s complexity. More importantly, the plethora of possibilities makes managing and planning an intricate ecosystem harder, from deciding how best to provide services based on present and the future requirements to where and how to deploy equipment and services in the most efficient way possible.
Also read: Is Machine Learning A Matter Of Fact AI?
AI/ML solutions would enable carriers to deliver the advantages of 5G to clients, as well as aligning to a CTO’s KPIs, beating the current situation in which monolithic, centralized networks strain themselves to deliver the advanced services clients are increasingly demanding.
AI solutions, of course, utilize machine learning to discover patterns in massive datasets, providing a clear image of cause and effect in resource usage to allow information enrichment for improved forecast and decision-making timing, demand, and a thousand other things that determine the quality of connections, whether voice or data. With such solutions, carriers will be able to get a complete and accurate picture of the state of the network. And based on current conditions and forthcoming requirements, AI/ML solutions can help carriers determine where and how to deploy resources to prevent demand crunches and potential service disruptions.
How can AI/ML improve network performance and unleash the entire potential of 5G? One of those matters artificial intelligence does nicely is effectively installed the technology, determining what requirements are needed for gear and calculations to perform most efficiently. To get MNOs, that could indicate assessing overall demand, using that data to plan for 5G and MIMO site configuration, base stations, and other hardware.
Artificial intelligence and machine learning will unlock the power of software and algorithms which will allow for efficient deployment of assets and resources. AI/ML solutions will have the ability to instantaneously analyze all relevant data and ascertain just how different elements of this network — hardware and software — interact, and how they perform under particular circumstances so that resources can be deployed to guarantee the quality of service and end-to-end latency control.
With that, the new technologies will not necessarily address a number of previous problems. According to a 2020 Swedish research, “Data safety related to wireless networks faces different challenges in comparison to traditional systems since it entails over-the-air data transmission which increases the risks of eavesdropping, fictitious base channels, and jamming attacks.”
Utilizing these approaches, hackers can create false info that”trusting” AI/ML systems will accept as precise and procedure as part of their database they operate from, thereby polluting the data and distributing incorrect info. Sophisticated hackers may thus completely compromise data on usage or demand, creating major gaps in service that may lead to large losses for customers as well as the carrier itself.
Another issue for AI/ML systems in 5G networks is the way the sheer volume of information will be analyzed. For machine learning to carry out its magic, it requires lots of information. The more the merrier and that data needs to be stored somewhere. Assuming considerable quantities of data will be uploaded not only by mobiles but by IoT devices also, carriers will need a lot more storage space than they currently have to be able to process that data.
Processing such large quantities of data will also require far quicker computers (a major expense for carriers), which might result in latency issues, maybe even canceling out the gains in latency realized with faster 5G technology. 1 approach to dealing with this really is the use of federated learning, where the model created by an AI/ML system for service development is tested against the data on edge servers, leading to much faster data processing times.
The key to all this, clearly, is automation. To realize the real benefits of AI/ML for 5G networks and all that they encircle, analysis ought to be automated, considering ever-changing conditions and altering usage demands and resource availabilities, along with anticipating future needs and pressures. Automated AI/ML programs are going to have the ability to offer a very clear and always-current picture of the condition of the 5G system, making sure that resources are set up as required, providing the best QoE possible, and unleashing the energy of 5G for the benefit of all users.