Since the early days of wireless communication, wireless spectrum has been allocated according to a static frequency plan, whereby most of the spectrum is licensed for exclusive use by specific services or radio technologies. While some spectrum bands are overcrowded, many other bands are heavily underutilized. As a result, there is a shortage of available spectrum to deploy emerging technologies that require high demands on data like 5G. Several global efforts address this problem by providing multi-tier spectrum sharing frameworks, for example, the Citizens Broadband Radio Service (CBRS) and Licensed Shared Access (LSA) models, to increase spectrum reuse. In these frameworks, the incumbent (i.e., the technology that used the spectrum exclusively in the past) has to be protected against service disruptions caused by the transmissions of the new technologies that start using the same spectrum. However, these approaches suffer from two main problems. First, spectrum re-allocation to new uses is a slow process that may take years. Second, they do not scale fast since it requires a centralized infrastructure to protect the incumbent and coordinate and grant access to the shared spectrum. As a solution, the Spectrum Collaboration Challenge (SC2) has shown that the collaborative intelligent radio networks (CIRNs) -- artificial intelligence (AI)-based autonomous wireless networks that collaborate -- can share and reuse spectrum efficiently without any coordination and with the guarantee of incumbent protection. In this article, we present the architectural design and the experimental validation of an incumbent protection system for the next generation of spectrum sharing frameworks. The proposed system is a two-step AI-based algorithm that recognizes, learns, and proactively predicts the incumbent's transmission pattern with an accuracy above 95 percent in near real time (less than 300 ms). The proposed algorithm was validated in Colosseum, the RF channel emulator built for the SC2 competition, using up to two incumbents simultaneously with different transmission patterns and sharing spectrum with up to five additional CIRNs.

中文翻译：

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基于人工智能的协作智能无线电网络现有保护系统


自无线通信早期以来，无线频谱就根据静态频率规划进行分配，其中大部分频谱被许可供特定服务或无线电技术独家使用。虽然某些频段过度拥挤，但许多其他频段却严重未得到充分利用。因此，可用频谱短缺，无法部署 5G 等对数据有高要求的新兴技术。一些全球性的努力通过提供多层频谱共享框架来解决这个问题，例如公民宽带无线电服务（CBRS）和许可共享接入（LSA）模型，以增加频谱重用。在这些框架中，必须保护现有技术（即过去专门使用该频谱的技术）免受因开始使用相同频谱的新技术传输而造成的服务中断。然而，这些方法存在两个主要问题。首先，频谱重新分配给新用途是一个缓慢的过程，可能需要数年时间。其次，它们无法快速扩展，因为它需要集中式基础设施来保护现有设备并协调和授予对共享频谱的访问权限。作为一种解决方案，频谱协作挑战赛 (SC2) 表明，协作智能无线电网络 (CIRN)——基于人工智能 (AI) 的协作自主无线网络——可以在没有任何协调的情况下高效地共享和重用频谱。现有保护的保证。在本文中，我们介绍了下一代频谱共享框架的现有保护系统的架构设计和实验验证。 所提出的系统是一种基于人工智能的两步算法，可以近乎实时（小于 300 毫秒）识别、学习并主动预测现任者的传输模式，准确率超过 95%。所提出的算法在 Colosseum 中得到了验证，Colosseum 是为 SC2 竞赛而构建的射频通道仿真器，同时使用最多两个具有不同传输模式的现有设备，并与最多五个附加 CIRN 共享频谱。