CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation

TL;DR

CeBed is a comprehensive benchmark platform that standardizes evaluation of deep learning methods for OFDM channel estimation, promoting reproducibility and fair comparison in wireless communication research.

Contribution

This work introduces CeBed, a standardized benchmark with datasets and baseline implementations for evaluating data-driven OFDM channel estimation methods.

Findings

Provides a unified framework for evaluation

Includes diverse datasets and baseline models

Addresses robustness and practical system considerations

Abstract

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.