Implementation of AI in Telecommunications

Telecom Business Review | Wednesday, April 28, 2021

The adoption of AI in telecommunications is at an all-time high right now, and the future looks even brighter. The global market for AI in telecommunications will reach an impressive $14.99B by the end of 2027.

FREMONT, CA: There have been several automation phases in the telecom industry. In the past, connections were still made by hand by switching cables. Later, hardware made this work easier by doing it automatically. These features no longer need specific hardware; they are almost entirely set by software.

Telecom infrastructures are critical to society, and more and more applications depend on them working well and being available all the time. Telecommunications has been using artificial intelligence (AI) for over a decade. Most AI applications today are focused on improving specific parameters.

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Getting a radio signal's parameters just right

Machine Learning (ML) is currently used to improve data flow to and from a Base Station (BTS) in a mobile network. The distance sets radio parameters to users, the number of connected users, and some environmental factors. In turn, they figure out the data that can be sent per amount of spectrum per unit of time. Interference also plays a role: micro and macro cells can share radio resources. For maximum efficiency, algorithms are used to figure out what part of the spectrum should be used by which user and with what settings. AI can be used to "tune" the way these algorithms work.

Power Management: Power savings in live mobile networks are made possible with the help of machine learning. Antennas actively change their radiation pattern, direction, and strength based on weather data, the number of users, and where they are. This saves energy, for example, at night when the need for data is low and makes better use of the base stations because they can cover a larger area at set-up points where the need for capacity is not the same.

Estimating the quality of the transmission: The signal can be messed up or stopped with optical connections, which can cause equipment to stop working. ML predicts how well the transmission will work over a connection. It determines the best path by considering the cable's length, other cables' signals, and the equipment's age. Based on this analysis, the traffic is sent where it should go. It's also possible that these algorithms are used in wireless networks, for example, to figure out how much error correction or redundancy (like retransmission) is used. Expert systems and ML algorithms are two types of AI used extensively in telecommunications. ML and distributed AI are two types of AI with the most potential for the future.

AI and ML are shaking up and changing how businesses work. Telecommunications companies can use these technologies to keep customers longer, let customers help themselves, improve equipment maintenance, and cut operational costs simultaneously.

The tech revolution and digital transformation are helping the telecommunications industry offer more services to its customers. But in today's digital world, consumers won't be happy with run-of-the-mill products and services. They also want better quality services and service providers who respond faster. These expectations can be met with the help of AI and ML-powered solutions that use data-driven insights.

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