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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Network performance optimisation

Network performance optimisation

Introduction

The Telecommunications industry is a highly integrated system which is expanding and becoming more sophisticated in order to address the increasing requirements for higher data rates and stronger network coverage. In order to address these challenges, the telecommunication companies are leveraging the use of Machine Learning (ML) and Artificial Intelligence (AI). The two technologies present a viable approach to network performance management and improvement thus allowing the industry to improve on efficiency, quality of service and profitability.

Challenges

There are several challenging areas in the telecommunications industry. Some of the major challenges include the optimization of network performance since there is a demand for higher data rates, lower latency and improved connectivity. This paper also identifies network traffic management as a challenge given the increasing volumes of data traffic. Other challenges are as follows: estimating and controlling the probability of network failures, providing quality of service and security concerns. Also, the advancement in the current network architecture, for instance 5G, creates challenges in the area of network slicing and resource management. Another challenge is the shortage of skilled personnel who can work with the large data that is being generated and make sense of it.

AI Solutions

In this paper, AI and ML are seen as solutions to the challenges facing the telecommunications industry. These are used in predictive analytics where the future in terms of network congestion and possible failure can be predicted so that preventive measures can be taken. The integration of AI algorithms enables the management of the network traffic to be more effective and in a way that is not likely to be congested. The current advanced level of ML is the use of anomaly detection where security threats are identified and dealt with. Some of the areas that can be augmented by AI include the automation of network operations and maintenance thus reducing the need for human intervention and improving accuracy. More specifically, the concept of AI has been applied in self-organizing networks (SONs) which are capable of changing and tuning themselves. For instance, Nokia’s ‘EdenNet SON’ and Ericsson’s ‘SON Optimization Manager’ are some of the examples.

Benefits

There are many advantages of AI and ML in the telecommunications sector. They enhance the efficiency and accuracy of the network performance management thus enhancing the quality of service and customer satisfaction. AI can perform boring tasks which makes the employees to work on more challenging tasks. This can minimize downtime which are the costs that are incurred in case of network failure. This is because intelligent traffic management can cater for the increasing data traffic without necessarily having to increase the physical infrastructure of the network. This is because security is improved through anomaly detection while at the same time, AI can be used in determining the most appropriate locations for new cell sites as well as the future demands of the network. ###

Return on Investment

There are significant return on investment when using artificial intelligence in network performance optimization. Although a significant amount of capital is required to purchase AI technology, the return on investment can be very high in the long run. In improving the network reliability and service quality, the telecommunication companies will be able to minimize on customer turnover and improve their image which is associated with increased revenue. This includes reducing operational costs through automation and predictive maintenance. In addition, it is possible to handle more data traffic and create new sources of revenue. For instance, AT&T has stated that it has been able to reduce costs by $200 million a year through the application of AI in optimizing and maintaining the network.