Deep learning has been extensively used in wireless communication problems, including channel estimation. Although several data-driven approaches exist, a fair and realistic comparison between them is difficult due to inconsistencies in the experimental conditions and the lack of a standardized experimental design. In addition, the performance of data-driven approaches is often compared based on empirical analysis. The lack of reproducibility and availability of standardized evaluation tools (e.g., datasets, codebases) hinder the development and progress of data-driven methods for channel estimation and wireless communication in general. In this work, we introduce an initiative to build benchmarks that unify several data-driven OFDM channel estimation approaches. Specifically, we present CeBed (a testbed for channel estimation) including different datasets covering various systems models and propagation conditions along with the implementation of ten deep and traditional baselines. This benchmark considers different practical aspects such as the robustness of the data-driven models, the number and the arrangement of pilots, and the number of receive antennas. This work offers a comprehensive and unified framework to help researchers evaluate and design data-driven channel estimation algorithms.

翻译：深度学习已广泛用于无线通信问题，包括信道估计。尽管存在多种数据驱动方法，但由于实验条件不一致且缺乏标准化实验设计，难以对这些方法进行公平且现实的比较。此外，数据驱动方法的性能常常基于经验分析进行对比。可重复性不足以及标准化评估工具（如数据集、代码库）的缺失，阻碍了数据驱动方法在信道估计乃至整个无线通信领域的发展与进步。本研究提出了一项基准建设计划，旨在统一多种数据驱动OFDM信道估计方法。具体而言，我们推出了CeBed（信道估计测试平台），该平台包含覆盖不同系统模型与传播条件的多样化数据集，以及十种深度学习与经典基线方法的实现。本基准考虑了多种实际因素，包括数据驱动模型的鲁棒性、导频数量与排列方式、以及接收天线数量。本研究提供了一个全面统一的框架，以帮助研究人员评估和设计数据驱动的信道估计算法。