论文标题
魅力：NextG频谱通过数据驱动的实时O-RAN动态控制
ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control
论文作者
论文摘要
当今的无线电访问网络（RANS）是整体实体，通常在其整个操作的给定参数上静态运行。为了实施现实有效的范围共享策略，RANS将需要无缝，智能地更改其操作参数。与现有范式形成鲜明对比的是，针对5G和beyond网络（NextG）的新的O-RAN体系结构将控制RAN与其硬件基板控制的逻辑分开，从而允许对RAN组件进行前所未有的实时细粒度控制。在这种情况下，我们提出了频道感知的反应性机制（CHARM），这是一个数据驱动的O-RAN兼容框架，允许（i）通过根据指定的频谱访问策略切换分布式单元（DU）和无线电单元（RU）来推断干扰的存在，以及（ii）实时反应。魅力基于直接在未加工的I/Q波形上运行的神经网络，以确定当前的光谱上下文。魅力不需要对现有3GPP标准进行任何修改。它旨在在O-RAN规范中运行，并且可以与其他频谱共享机制（例如LTE-U，LTE-LAA或Multefire）一起使用。我们证明了在LTE和Wi-Fi之间在未经许可的频段中共享频谱共享的魅力的性能，在该频段中，通过RAN智能控制器（RIC）运行的控制器会感觉到光谱并切换单元格频率以避免使用Wi-Fi。我们使用SRSRAN开发了一个魅力的原型，并利用罗马斗兽场通道模拟器收集大规模的波形数据集来使用我们的神经网络来训练我们的神经网络。实验结果表明，魅力在罗马竞技场上的准确性高达96％，在空中测试床上达到了85％，这表明了魅力的能力利用了所考虑的光谱通道。
Today's radio access networks (RANs) are monolithic entities which often operate statically on a given set of parameters for the entirety of their operations. To implement realistic and effective spectrum sharing policies, RANs will need to seamlessly and intelligently change their operational parameters. In stark contrast with existing paradigms, the new O-RAN architectures for 5G-and-beyond networks (NextG) separate the logic that controls the RAN from its hardware substrate, allowing unprecedented real-time fine-grained control of RAN components. In this context, we propose the Channel-Aware Reactive Mechanism (ChARM), a data-driven O-RAN-compliant framework that allows (i) sensing the spectrum to infer the presence of interference and (ii) reacting in real time by switching the distributed unit (DU) and radio unit (RU) operational parameters according to a specified spectrum access policy. ChARM is based on neural networks operating directly on unprocessed I/Q waveforms to determine the current spectrum context. ChARM does not require any modification to the existing 3GPP standards. It is designed to operate within the O-RAN specifications, and can be used in conjunction with other spectrum sharing mechanisms (e.g., LTE-U, LTE-LAA or MulteFire). We demonstrate the performance of ChARM in the context of spectrum sharing among LTE and Wi-Fi in unlicensed bands, where a controller operating over a RAN Intelligent Controller (RIC) senses the spectrum and switches cell frequency to avoid Wi-Fi. We develop a prototype of ChARM using srsRAN, and leverage the Colosseum channel emulator to collect a large-scale waveform dataset to train our neural networks with. Experimental results show that ChARM achieves accuracy of up to 96% on Colosseum and 85% on an over-the-air testbed, demonstrating the capacity of ChARM to exploit the considered spectrum channels.
