The growth in wireless broadband users, devices, and novel applications has led to a significant increase in the demand for new radio frequency spectrum. This is expected to grow even further given the projection that the global traffic per year will reach 4.8 zettabytes by 2022. Moreover, it is projected that the number of Internet users will reach 4.8 billion and the number of connected devices will be close 28.5 billion devices. However, due to the spectrum being mostly allocated and divided, providing more spectrum to expand existing services or offer new ones has become more challenging. To address this, spectrum sharing has been proposed as a potential solution to improve spectrum utilization efficiency. Adopting effective and efficient spectrum sharing mechanisms is in itself a challenging task given the multitude of levels and techniques that can be integrated to enable it. To that end, this paper provides an overview of the different spectrum sharing levels and techniques that have been proposed in the literature. Moreover, it discusses the potential of adopting dynamic sharing mechanisms by offering Spectrum-as-a-Service architecture. Furthermore, it describes the potential role of machine learning models in facilitating the automated and efficient dynamic sharing of the spectrum and offering Spectrum-as-a-Service.

中文翻译：

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机器学习实现频谱即服务动态共享

无线宽带用户、设备和新型应用的增长导致对新无线电频谱的需求显着增加。鉴于预计到 2022 年全球每年的流量将达到 4.8 泽字节，预计这一数字还会进一步增长。此外，预计互联网用户数量将达到 48 亿，连接设备数量将接近 285 亿台. 然而，由于频谱大部分被分配和划分，提供更多频谱以扩展现有服务或提供新服务变得更具挑战性。为了解决这个问题，频谱共享已被提议作为提高频谱利用效率的潜在解决方案。考虑到可以集成以实现它的众多级别和技术，采用有效和高效的频谱共享机制本身就是一项具有挑战性的任务。为此，本文概述了文献中提出的不同频谱共享级别和技术。此外，它还讨论了通过提供频谱即服务架构采用动态共享机制的潜力。此外，它还描述了机器学习模型在促进频谱的自动化和高效动态共享以及提供频谱即服务方面的潜在作用。它讨论了通过提供频谱即服务架构采用动态共享机制的潜力。此外，它还描述了机器学习模型在促进频谱的自动化和高效动态共享以及提供频谱即服务方面的潜在作用。它讨论了通过提供频谱即服务架构采用动态共享机制的潜力。此外，它还描述了机器学习模型在促进频谱的自动化和高效动态共享以及提供频谱即服务方面的潜在作用。