Explainable AI for 6G Communication Networks

Artificial intelligence is increasingly integrated into future communication networks, yet many AI models operate as opaque black boxes, raising concerns about reliability, transparency, and security. For 6G, semantic communication, cognitive networking, and integrated sensing-communication systems, explainability is essential to ensure trustworthy decision-making and safe deployment. This Research Topic focuses on explainable and trustworthy AI techniques tailored for communication and networking systems. It invites contributions on interpretable model design, transparent network optimization, digital twin–based explainability, explainable multi-agent systems, and practical frameworks that enhance robustness, fairness, and trust. The aim is to advance transparent, reliable, and accountable AI to support next-generation intelligent networks.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Classification
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Classification
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Explainable Artificial Intelligence, Semantic Communication, Network Optimization, Digital Twin, AI Network Slicing, LLMs

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.