Beyond 5G: Big Data Processing for Better Spectrum Utilization

TL;DR

This paper explores how big data processing of detailed Radio Service Maps can enhance spectrum utilization in future 6G networks by enabling more effective, context-aware dynamic spectrum management in convergent wireless systems.

Contribution

It introduces a novel RSM-based architecture for DSM in 6G and IoT networks, highlighting the importance of rich context information for improved spectral efficiency.

Findings

RSM enables significant performance improvements in spectrum management.

Effective big data algorithms are essential for future wireless applications.

Context-aware RSM architecture supports convergent 6G and IoT networks.

Abstract

This article emphasizes the great potential of big data processing for advanced user- and situation-oriented, so context-aware resource utilization in future wireless networks. In particular, we consider the application of dedicated, detailed, and rich-in-content maps and records called Radio Service Maps, (RSM) for unlocking the spectrum opportunities in 6G networks. Due to the characteristics of 5G, in the future, there will be a need for high convergence of various types of wireless networks, such as cellular and the Internet-of-Things (IoT) networks, which are steadily growing and consequently considered as the studied use case in this work. We show that the 6G network significantly benefits from effective Dynamic Spectrum management (DSM) based on RSM which provides rich and accurate knowledge of the radio context; a knowledge that is stored and processed within database-oriented subsystems designed to support wireless networks for improving spectral efficiency. In this article, we discuss context-aware RSM subsystem architecture and operation for DSM in convergent 6G radio and IoT networks. By providing various use-cases, we demonstrate that the accurate definition and access to the rich context information lead to a significant improvement of the system performance. In consequence, we also claim that efficient big-data processing algorithms will be necessary for future applications.