From Multilayer Perceptron to GPT: A Reflection on Deep Learning Research for Wireless Physical Layer

TL;DR

This paper emphasizes the importance of the accuracy-generalization trade-off in deep learning for wireless physical layer systems, proposing evaluation guidelines to improve research relevance and practical deployment.

Contribution

It highlights the critical trade-offs in DL model design for wireless communication and offers evaluation guidelines to bridge empirical research with system requirements.

Findings

Revisits early DL-based wireless communication research

Identifies key trade-offs: accuracy vs. generalization, compression vs. latency

Proposes evaluation guidelines for practical impact

Abstract

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.