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.

中文翻译：

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超越 5G：大数据处理以提高频谱利用率

本文强调了大数据处理对于高级用户和面向情境的巨大潜力，因此未来无线网络中的上下文感知资源利用。特别是，我们考虑应用称为无线电服务地图 (RSM) 的专用、详细和内容丰富的地图和记录，以释放 6G 网络中的频谱机会。由于 5G 的特点，未来需要各种无线网络的高度融合，如蜂窝和物联网 (IoT) 网络，这些网络正在稳步增长，因此被认为是在这项工作中研究了用例。我们表明 6G 网络显着受益于基于 RSM 的有效动态频谱管理 (DSM)，它提供了丰富而准确的无线电环境知识；在面向数据库的子系统中存储和处理的知识，旨在支持无线网络以提高频谱效率。在本文中，我们将讨论融合 6G 无线电和物联网网络中 DSM 的上下文感知 RSM 子系统架构和操作。通过提供各种用例，我们证明了对丰富上下文信息的准确定义和访问可以显着提高系统性能。因此，我们还声称，未来的应用将需要高效的大数据处理算法。通过提供各种用例，我们证明了对丰富上下文信息的准确定义和访问可以显着提高系统性能。因此，我们还声称，未来的应用将需要高效的大数据处理算法。通过提供各种用例，我们证明了对丰富上下文信息的准确定义和访问可以显着提高系统性能。因此，我们还声称，未来的应用将需要高效的大数据处理算法。