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.

中文翻译：

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从多层感知器到GPT：无线物理层深度学习研究的反思


大多数关于应用于无线通信物理层的深度学习（DL）的研究并没有提出准确性与泛化权衡在开发和评估实用算法中的关键作用。为了强调这种常见做法的缺点，我们回顾了第一篇论文中的数据解码示例，该论文向研究界介绍了基于深度学习的端到端无线通信系统，并推广了人工智能 (AI)/深度学习的使用无线物理层。然后，我们在设计用于通信的深度学习模型时提出了两个关键权衡，即准确性与泛化以及压缩与延迟。我们讨论了它们在使用新兴深度学习模型（包括大型语言模型 (LLMs)）的无线通信用例背景下的相关性。最后，我们总结了我们提出的评估指南，以增强深度学习对无线通信的研究影响。这些指南试图协调深度学习研究的实证性质与无线通信系统的严格要求指标。