Artificial Intelligence to improve the availability of Mission Critical communication

Abstract

In modern society, critical operations, such as emergency response and public safety, rely on communication systems, in this context also referred to as mission critical systems. These systems must meet strict availability requirements, since any failure can lead to severe consequences, including loss of life. Traditionally, dedicated private communication systems, like local Push-to-Talk systems, were used for such critical operations, but there is a growing shift towards utilizing public 4G and 5G mobile networks to achieve better coverage, higher data speeds and more innovative features at a lower cost.

With the increasing complexity and vast amount of data from the network, automation and artificial intelligence (AI) are now becoming essential tools in these communication systems to efficiently manage data, configure networks in real-time, and effectively handle alarms. The use of AI can improve the end-to-end availability of mission critical systems, ensuring communication during critical situations.

The main goal of this research is to investigate whether and how the use of AI can improve the end-to-end availability of mission critical systems, with a specific focus on the Mission Critical Push-to-Talk (MCPTT) system of KPN, which is using the public 4G and 5G network. Currently, the KPN MCPTT system is being used with a relatively limited number of users. However, the vision for MCPTT extends beyond its current implementation, aiming to scale up this service. With an increasing number of users, using automation and AI is essential for optimizing and managing the complexities of this mission critical communication system.

The implementation of AI in the MCPTT system follows a systematic approach, starting with the independent analysis and monitoring of system specific elements. By focusing on these elements and using data such as Call Detail Records (CDR) and log data, insights into the system's behavior can be obtained. Through collaboration with system experts, AI algorithms can be trained to effectively detect anomalies, thereby enhancing the overall availability of the MCPTT system. Looking ahead, the integration of real-time data becomes crucial for proactive monitoring. Establishing a streamlined data pipeline facilitates the flow of real-time information, offering a comprehensive overview of system performance and enabling swift anomaly detection. It is concluded that the monitoring of individual system elements with the use of AI is a first step towards improving the end-to-end availability.

In order to ensure the correct use of AI throughout the complete cycle, it is crucial to look at explainability, safety, and data quality. These points should be included at each stage of the AI process. By ensuring explainability, system experts can gain insights into the decision-making process of the AI algorithms. By using safety mechanisms, potential risks and vulnerabilities can be mitigated. Maintaining data quality is essential to achieve accurate outcomes.