An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications

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

Random forests for resource allocation in 5G cloud radio access networks based on position information

Abstract Next generation 5G cellular networks are envisioned to accommodate an unprecedented massive amount of Internet of things (IoT) and user devices while providing high aggregate multi-user sum rates and low latencies. To this end, cloud radio access networks (CRAN), which operate at short radio frames and coordinate dense sets of spatially distributed radio heads, have been proposed. However, coordination of spatially and temporally denser resources for larger sets of user population implies considerable resource allocation complexity and significant system signalling overhead when associated with channel state information (CSI)-based resource allocation (RA) schemes. In this paper, we propose a novel solution that utilizes random forests as supervised machine learning approach to determine the resource allocation in multi-antenna CRAN systems based primarily on the position information of user terminals. Our simulation studies show that the proposed learning based RA scheme performs comparably to a CSI-based scheme in terms of spectral efficiency and is a promising approach to master the complexity in future cellular networks. When taking the system overhead into account, the proposed learning-based RA scheme, which utilizes position information, outperforms legacy CSI-based scheme by up to 100%. The most important factor influencing the performance of the proposed learning-based RA scheme is antenna orientation randomness and position inaccuracies. While the proposed random forests scheme is robust against position inaccuracies and changes in the propagation scenario, we complement our scheme with three approaches that restore most of the original performance when facing random antenna orientations of the user terminal

The Self-Growing Concept as a Design Principle of Cognitive Self-Organization

Abstract — In next generation systems and networks selforganization in networks of collaborating networks is expected to relax some of the intricacies of managing complex cooperative communication systems. In particular, in the presence of distributed cognitive decision-making, increasing complexity may increase potential interference between collaborating networks hence leading to performance, robustness and dependability issues. This paper focuses on a specific form of self-organization denoted here as self-growing, which is believed to provide a foundation for flexible, open and trustworthy networks, relax some of the scalability issues of collaborating cognitive networks, as well as to enable selforganization for resource constrained systems