AI-Oriented 6G Networks

Dear Colleagues,

As artificial intelligence increasingly shapes the requirements of global digital infrastructure, sixth-generation (6G) networks are anticipated to function as the foundational substrate for large-scale AI ecosystems. This Special Issue on “AI-Oriented 6G Networks” focuses on the design, optimization, and architectural evolution of 6G explicitly to enable, accelerate, and sustain AI services. In contrast to AI-empowered networking—where AI algorithms optimize communications—this collection targets the reciprocal challenge: engineering communication, computation, and sensing systems that inherently support distributed learning, real-time inference, and large-model operation across pervasive devices and intelligent environments.

The scope covers theoretical, algorithmic, and system-level developments addressing key AI-centric requirements. Representative themes include communication-efficient distributed and federated learning; networking architectures that integrate edge, device, and in-network computing; compute-communication co-design for AI service guarantees; multi-modal sensing–communication integration; data- and model-flow optimization; and infrastructure strategies for low-latency synchronization and reliable exchange of large AI models. Contributions that investigate how 6G can meet the stringent demands of foundation-model training, multi-agent intelligence, cooperative robotics, and immersive AI applications are especially welcomed.

While the vision of “6G for AI” is now an established and rapidly evolving research direction, the literature remains distributed across multiple communities—wireless communication theory, distributed systems, computer architecture, and AI/ML. This Special Issue aims to consolidate these threads into a coherent body of work. By curating research that treats AI workloads as primary design drivers rather than secondary use cases, the collection seeks to advance unified frameworks, performance analyses, and architectural principles toward a robust and scalable AI-oriented 6G infrastructure.

Dr. Bin Han
Dr. Wei Jiang
Guest Editors

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• AI-oriented 6G
• 6G for AI services
• communication-efficient distributed learning
• edge and in-network computing
• compute–communication co-design
• model distribution and synchronization
• data-centric architectures
• networked AI systems
• scalable intelligent infrastructure