# Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues arXiv: https://arxiv.org/abs/2303.08106 ยท HTML: https://mlsanity.com/paper/2303.08106/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 116 of its 204 references that are in the map. - First posted on arXiv 13 Mar 2023. ## Builds on (most cited first, 30 of 116) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017) - [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017) - [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013) - [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019) - [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019) - [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017) - [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016) - [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020) - [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018) - [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018) - [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019) - [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020) - [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016) - [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017) - [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020) - [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021) - [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021) - [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017) - [Out-of-Distribution Generalization via Risk Extrapolation (REx)](https://mlsanity.com/paper/2003.00688/) (2020) - [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018) - [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013) - [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014) - [Domain Generalization with MixStyle](https://mlsanity.com/paper/2104.02008/) (2021) - [Generalizing to Unseen Domains via Adversarial Data Augmentation](https://mlsanity.com/paper/1805.12018/) (2018) - [SWAD: Domain Generalization by Seeking Flat Minima](https://mlsanity.com/paper/2102.08604/) (2021) - [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017) - [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018) - [Learning to Learn Single Domain Generalization](https://mlsanity.com/paper/2003.13216/) (2020) ## Built on by (most cited first, 12 of 12) - [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024) - [HeterCSI: Channel-Adaptive Heterogeneous CSI Pretraining Framework for Generalized Wireless Foundation Models](https://mlsanity.com/paper/2601.18200/) (2026) - [Robust ADAS: Enhancing Robustness of Machine Learning-based Advanced Driver Assistance Systems for Adverse Weather](https://mlsanity.com/paper/2407.02581/) (2024) - [Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks](https://mlsanity.com/paper/2502.12736/) (2025) - [Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores](https://mlsanity.com/paper/2512.01080/) (2025) - [Koopman-Based Generalization of Deep Reinforcement Learning With Application to Wireless Communications](https://mlsanity.com/paper/2503.02961/) (2025) - [Achieving Robust Channel Estimation Neural Networks by Designed Training Data](https://mlsanity.com/paper/2507.12630/) (2025) - [Science-Informed Design of Deep Learning With Applications to Wireless Systems: A Tutorial](https://mlsanity.com/paper/2407.07742/) (2024) - [Disturbance-based Discretization, Differentiable IDS Channel, and an IDS-Correcting Code for DNA-based Storage](https://mlsanity.com/paper/2407.18929/) (2024) - [A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding](https://mlsanity.com/paper/2601.21897/) (2026) - [Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity](https://mlsanity.com/paper/2602.18151/) (2026) - [IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing](https://mlsanity.com/paper/2609.06131/) (2026) ## Abstract (arXiv) Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations 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.