This research addresses the critical problem of wireless hallucinations - when GenAI models generate physically impossible or unrealistic wireless communication data that compromises system reliability.

• Identifies three main causes: limited training data, random generation mechanisms, and dynamic environments
• Proposes a comprehensive taxonomy to classify wireless hallucinations
• Introduces novel detection and mitigation techniques to maintain communication reliability
• Outlines future research directions for robust GenAI-enabled wireless communications

This work is significant for engineering as it establishes foundational principles to ensure robust, reliable AI integration in next-generation wireless networks while preventing costly system failures due to AI-generated misinformation.

Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions