Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges

Abstract

Next-generation wireless systems are expected to be ``AI-native," with neural networks (NNs) embedded throughout the physical (PHY) layer protocol stack to improve spectral efficiency, latency, and network autonomy. However, the opacity of deep learning (DL) models raises increasing concerns about system reliability, safety, and privacy, especially under complex and time-varying network environments. This survey studies explainable AI (XAI) in wireless PHY layers from the explainability perspective. We first formalize a series of responsibility-oriented goals for wireless XAI. Then, we develop a systematic taxonomy of explainability approaches and distill practical criteria for deploying explanations in communication scenarios. We provide a comprehensive review of where and how XAI can be applied throughout the PHY layer, connecting representative learning paradigms to appropriate explanation techniques, evaluation metrics, and deployment considerations. Open challenges and future directions are discussed, including explainability-performance tradeoffs, explainability-aware data processing, customized XAI for communication-specific structures, cross-layer explanation consistency, and emerging needs for explaining LLM- and Agentic-AI-driven PHY layers.