An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications

Abstract

The purpose of this survey study is to shed light on the importance of knowledge usage and knowledge-driven applications in telecommunication systems and businesses. To this end, we first define a classification of the different knowledge-based approaches in terms of knowledge representations and reasoning formalisms. Further, we define a set of qualitative criteria and evaluate the different categories for their suitability and usefulness in telecommunications. From the evaluation results, we could conclude that different use cases are better served by different knowledge-based approaches. Further, we elaborate and showcase our findings on three different knowledge-based approaches and their applicability to three operational aspects of telecommunication networks. More specifically, we study the utilization of large language models in network operation and management, the automation of the network based on knowledge-graphs and intent-based networking, and the optimization of the network based on machine learning-based distributed intelligence. The article concludes with challenges, limitations, and future steps toward knowledge-driven telecommunications.

Keywords

• network automation
• generative AI
• large language models
• knowledge-based systems
• knowledge representation
• machine reasoning