  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.
