Machine Learning
Machine Learning

Machine Learning – How to Boost 5G Network Performance

The conversion from 3G to 4G over the past few years saw the deployment of more cells in various form factors, as well as new technologies, such as carrier aggregation and full-packet switching, made 4G networks more efficient than 3G. Made efficient Made complicated. More complex but still manageable.


More equipment, more advanced technology, more coverage and capabilities. There is a clear line of progress as each new generation of wireless technology comes into use from 3G via LTE and advanced 4G, and now 5G is the next big thing in wireless.

The 5G network will see the deployment of an order of magnitude with more cells (4G LTE transferred to the existing fabric of macro-cells) and antennas (both passive and active ones that use multiple frequencies), and more advanced technologies such as With VANN, which will allow partial or full virtualization of the network, allowing for faster and cheaper upgrades and innovations in service.

5G will allow the introduction of other technologies to handle a huge increase in capabilities, all in a multilayer environment – with an emphasis on enabling ultra-low-latency data transmission that 5G is designed for.

AI solutions, of course, use machine learning to discover patterns in large datasets, enabling data forecasting for a thousand other factors that determine better predictability and decision-making time, demand, and quality. Provide a clear picture of cause and effect in resource utilization. Of connection, whether voice or data. But AI / ML will do a lot for 5G network. Artificial intelligence and machine learning will unlock the power of software and algorithms that allow for efficient deployment of assets and resources. AI / ML solutions will be able to quickly analyze all relevant data and determine how different elements of the network – hardware and software – interact, and how they perform under specific circumstances, to ensure quality and end of service Resources can be deployed — end-to-end latency control.

Using these methods, hackers can generate inaccurate data that “trust” AI / ML systems will accept as part of the database as accurate and the process by which they operate, thus polluting the data and Give incorrect information. Sophisticated hackers can thus completely compromise data on usage or demand, creating major gaps in service that can cause major losses for customers and carriers themselves.

Another issue for AI / ML systems in 5G networks is how the sheer volume of data will be analyzed. For machine learning to run its magic, it requires a lot of data. The more the merger, the more data has to be stored somewhere.

The key to all of this, of course, is automation. In order to realize the real benefits of AI / ML for 5G networks and all those in anticipation of future needs and pressures, as well as changing circumstances and resource uses, the analysis should be automated. Automated AI / ML systems will be able to provide a clear and ever-present picture of the state of the 5G network, providing 5G power for the benefit of all users, ensuring that resources are deployed as needed, and Getting the power of 5G.

Photo by Markus Winkler

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