Learning-based techniques such as artificial intelligence (AI) and machine learning (ML) play an increasingly important role in the development of future communication networks. The success of a learning algorithm depends on the quality and quantity of the available training data. In the physical layer (PHY), channel information data can be obtained either through measurement campaigns or through simulations based on predefined channel models. Performing measurements can be time consuming while only gaining information about one specific position or scenario. Simulated data, on the other hand, are more generalized and reflect in most cases not a real environment but instead, a statistical approximation based on a mathematical model. This paper presents a procedure for acquiring channel data by means of fast and flexible software defined radio (SDR) based channel measurements along with a method for a parameter extraction that provides configuration input to the simulator. The procedure from the measurement to the simulated channel data is demonstrated in two exemplary propagation scenarios. It is shown, that in both cases the simulated data is in good accordance to the measurements

翻译：基于学习的技术，如人工智能（AI）和机器学习（ML），在未来通信网络的发展中扮演着越来越重要的角色。学习算法的成功取决于可用训练数据的质量和数量。在物理层（PHY），信道信息数据可以通过测量活动或基于预定义信道模型的仿真来获取。进行测量可能耗时，且仅能获取关于特定位置或场景的信息。另一方面，仿真数据更为通用，但在大多数情况下反映的并非真实环境，而是基于数学模型的统计近似。本文提出了一种通过快速灵活的软件定义无线电（SDR）进行信道测量的数据采集流程，以及一种参数提取方法，为仿真器提供配置输入。在两个典型的传播场景中，展示了从测量到仿真信道数据的完整流程。结果表明，在这两种情况下，仿真数据与测量结果具有良好的一致性。