  Most research studies on deep learning (DL) applied to the physical layer of
wireless communication do not put forward the critical role of the
accuracy-generalization trade-off in developing and evaluating practical
algorithms. To highlight the disadvantage of this common practice, we revisit a
data decoding example from one of the first papers introducing DL-based
end-to-end wireless communication systems to the research community and
promoting the use of artificial intelligence (AI)/DL for the wireless physical
layer. We then put forward two key trade-offs in designing DL models for
communication, namely, accuracy versus generalization and compression versus
latency. We discuss their relevance in the context of wireless communications
use cases using emerging DL models including large language models (LLMs).
Finally, we summarize our proposed evaluation guidelines to enhance the
research impact of DL on wireless communications. These guidelines are an
attempt to reconcile the empirical nature of DL research with the rigorous
requirement metrics of wireless communications systems.
