Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generation wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.

中文翻译：

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无线通信机器学习模型的领域泛化：概念、最新技术和悬而未决的问题

数据驱动的机器学习 (ML) 被推广为下一代无线系统中使用的一项潜在技术。这导致了大量的研究工作应用机器学习技术来解决无线传输链路不同层的问题。然而，这些应用程序大多数依赖于监督学习，它假设源（训练）和目标（测试）数据是独立且同分布（iid）的。由于源数据和目标数据之间的域或分布变化，这一假设在现实世界中经常被违反。因此，确保这些算法能够推广到分布外 (OOD) 数据非常重要。在这种情况下，域泛化（DG）通过学习不同且不同的源域/数据集上的模型来解决与 OOD 相关的问题，并具有泛化能力到未见过的新域，而无需额外的微调。鉴于 DG 要求对无线应用的重要性，我们全面概述了 DG 的最新发展以及域转移的不同来源。我们还总结了现有的 DG 方法并回顾了它们在选定的无线通信问题中的应用，并以见解和开放性问题作为总结。