  Joint channel estimation and signal detection (JCESD) is crucial in
orthogonal frequency division multiplexing (OFDM) systems, but traditional
algorithms perform poorly in low signal-to-noise ratio (SNR) scenarios. Deep
learning (DL) methods have been investigated, but concerns regarding
computational expense and lack of validation in low-SNR settings remain. Hence,
the development of a robust and low-complexity model that can deliver excellent
performance across a wide range of SNRs is highly desirable. In this paper, we
aim to establish a benchmark where traditional algorithms and DL methods are
validated on different channel models, Doppler, and SNR settings, particularly
focusing on the semi-blind setting. In particular, we propose a new DL model
where the backbone network is formed by unrolling the iterative algorithm, and
the hyperparameters are estimated by hypernetworks. Additionally, we adapt a
lightweight DenseNet to the task of JCESD for comparison. We evaluate different
methods in three aspects: generalization in terms of bit error rate (BER),
robustness, and complexity. Our results indicate that DL approaches outperform
traditional algorithms in the challenging low-SNR setting, while the iterative
algorithm performs better in high-SNR settings. Furthermore, the iterative
algorithm is more robust in the presence of carrier frequency offset, whereas
DL methods excel when signals are corrupted by asymmetric Gaussian noise.
