Artificial Intelligence in Telecommunication

Artificial intelligence in telecommunication has led to several automation phases since its inception. Although previous connections were still done manually by swapping wires, hardware soon automated this operation. These functionalities are now mostly defined by software and do not require specific hardware.


Since around 2010, artificial intelligence (AI) has been used in the telecommunications industry and recording a positive improvement. Besides the fact that telecom infrastructures are essential to society, an increasing number of applications rely on efficient, dependable, and always accessible telecom services.


The majority of current AI applications concentrate on enhancing particular metrics.


Specific AI Applications


Optimizing Radio Signal Parameters


Presently, machine learning (ML) is employed in mobile networks to enhance the flow of information to and from base stations (BTS). The radio parameters depend on the users’ proximity, connectivity, and a few environmental conditions. The maximum amount of data that may be transmitted per unit of time and spectrum is then determined by them. Since radio resources can be coordinated between macro and micro cells, interference also has an impact. Algorithms are being used to decide which portion of the spectrum should be used for which user and with which parameters in order to improve efficiency. AI can be used to tune these algorithms’ settings.


Power Control


In active mobile networks, power saving is achieved via machine learning algorithms. Antennas actively change their radiation pattern, direction, and strength to meet demand based on meteorological information, the number of users, and their location. Because a larger surface area may be operated at setup locations where the capacity demand is not uniform, energy savings result, for example, during the night when data consumption is relatively low, and in more effective use of the base stations.


Estimation of Transmission Quality


With optical connections, transmission can be hampered or cut off, which may cause device failure that is irreversible. Machine learning is used to predict in advance how well the transmission will function over a connection. It calculates the best path based on criteria like as cable length, other signals present in the cable, and the age of the equipment. Based on this evaluation, the traffic is routed. Such algorithms might also be employed in wireless networks too, for instance, deciding how much error correction or redundancy (such as retransmission) is required. The two AI methods most commonly utilized in the telecommunications industry are expert networks and deep learning algorithms, whereas the two AI methods with the best future prospects are distributed machine learning and AI.


AI and Machine Learning


Companies are being disrupted and transformed by AI and ML. These innovations can be used by telecommunications businesses to enhance consumer retention, enable self-service, enhance equipment maintenance, and cut operational expenses all at once.


The telecommunications sector is embracing the digital transformation and tech revolution to provide a larger range of services to its customers. Consumers in today’s digital age are not be satisfied with standard goods and services. They demand higher standards of services and more receptive service providers. Telecom providers can meet these expectations by using data-driven insights supported by AI and ML-powered solutions.


Autonomous Learning and Action


Applications of AI frequently replace or assist humans in jobs that are typically undertaken by humans. This shows that these systems possess some autonomy. The two sorts of independence are independent learning and autonomous action.


Autonomous Education


The AI model used today was created using a significant amount of (historical) data. With a certain input, an algorithm “learns” the desired outcomes from this data. There are various ways to mold this education or “training” model:



●Offline Learning - Offline learning involves training a model once or occasionally using a static dataset. Before the model is utilized in production, both the data and the model are checked and validated.

●Online Learning: The model is treated similarly to offline learning but frequently updated based on fresh data.

●Continuous Learning: Using incoming data, the model is updated continuously. There are no longer different “versions” of the model, as there were with online learning. Instead, each inference request may have a direct impact on the next AI decision.


Autonomous Action


There are several ways that AI applications in the telecommunications sector can be used. The most popular ones comprise: 



Closed-loop scenario - in this case, the AI system immediately carries out the activity. The only thing users can do is turn off the system, like the speech recognition software.

Open-loop scenario - In an open-loop scenario, the AI system’s function is to offer help. A human can act in response to the outcome that AI provides to them. Here, users have the option to disregard the advice or to verify it using further data. Expert systems that assist doctors in making diagnoses is one example.

An AI system can act directly in a closed loop that is constrained by rules, but still subject to those “hard” restrictions. The system becomes inactive or does nothing when rules are broken. Autonomous vehicles is one example using this tactic. They frequently have several “fail safe” features that guarantee an automobile will perform an emergency stop in risky circumstances.

In the human-in-the-loop scenario, AI can take direct action, but humans can halt or modify these activities as needed. As an illustration, consider autonomous vehicles that need drivers to maintain their hands on the wheel.

An AI system that conducts actions is being watched over by one or more other AI systems. The controlling Ai system can examine the initial inputs and the AI’s choice and determine whether it was the right one.


The benefit of Artificial Intelligence in the Telecommunications Industry


The applications of AI in telecom can produce a ton of added value for both consumers and operators while helping resolve several difficult and occasionally protracted problems. The latter has consistently gathered significant amounts of telemetry and data on service usage, but most of it was never utilized in a useful way because of a lack of an appropriate software.


With AI, this vast collection of idle data may be transformed into fertile ground for developing new services, enhancing the quality of current ones, elevating the customer experience, and streamlining company processes. According to relatively recent research, AI will generate approximately 11 billion dollars for telecom firms by 2025. This is a remarkable sum that is projected to keep increasing as the range of AI applications widens.


AI offers the following benefits and opportunities:


AI Can Decide More Quickly and Sometimes Better Than Humans


A machine learning algorithm can frequently process thousands of data pieces in a short amount of time while a person may occasionally need a few minutes to come to a judgement.


For instance, fraud detection AI can keep track of thousands of credit card transactions in real time and stop transactions that might be fraudulent.


Rare Expert Knowledge can be used more Effectively Using AI


Several years of training are frequently required before someone enters the workforce. After a few years later, a person won’t be at the peak of the game. With ML, an expert’s knowledge may be condensed into a model which allows for wider application of that knowledge.


Artificial Intelligence is Proficient at Repetitive Tasks


Repetitive tasks are frequently seen as uninteresting and unfulfilling by people. But if these jobs are structured properly, the AI framework is perfectly prepared to take over. AI won’t grow bored or fatigued, so it won’t need to sleep, rest, or take breaks. 

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