This paper proposes a protocol framework for making the 6G air interface AI-native, focusing on interoperability and preserving implementation freedom.

This paper presents a tutorial-and-survey on integrating agentic AI into Next-Generation Networks (NGNs), addressing the gap in protocol integration, evaluation, and standardization alignment.

This paper proposes a new communication framework, TokCom, for 6G wireless networks where tokens from large language models become the fundamental entities for information exchange.

This survey examines explainable AI (XAI) in wireless PHY layers, formalizing goals, taxonomy, and applications.

The paper introduces The AI Telco Engineer (AITE), a framework for autonomously designing wireless communication algorithms using large language models, achieving better performance and reduced latenc…

The paper introduces RadioMaster, a novel multi-agent system that successfully translates high-level user intents into physically viable, real-world radio signals, significantly outperforming existing…

This paper proposes Autogenic network management, a self-programming extension to agentic AI for next-generation network management in 6G networks.

This paper proposes a definition for 'AI-nativeness' in systems, based on an AI agent's authority over system decisions.

This paper surveys model forensics in AI-native wireless networks, detailing key security problems and demonstrating practical workflows for verifying model authenticity and detecting malicious functi…

The paper proposes AgentxGCore, an Agentic AI-Native layer that extends the 3GPP core network to enable self-organizing, self-adapting, and continuously optimized network management for 6G.

This paper presents a comprehensive survey of security and privacy in AI-native 6G networks from a cross-layer perspective, developing a threat taxonomy, analyzing representative threats, and identify…

Proposed EvoOMG framework optimizes throughput in heterogeneous Wi-Fi networks with both legacy and MLO-capable stations using an evolution-oriented multi-agent guidance approach.

The paper proposes a framework to harvest unused computation resources on AI chips for general-purpose tasks using neural architecture search and approximation techniques.

The paper proposes GUIDE, a physics-guided deep unfolding framework that enables practical, real-time cross-band channel prediction for AI-RAN by embedding wireless channel physics, significantly impr…

This paper proposes a lightweight, distributed machine learning framework for sub-centimeter indoor localization using D-MIMO in O-RAN architectures, reducing midhaul traffic by 100x while maintaining…

This paper proposes a framework for compositional semantic communication (CSC) in physical AI systems, enabling heterogeneous devices to transmit semantic representations that compose meaningfully at…

This paper proposes SubEdge, a Net4AI subsystem for per-subscriber edge computing and communication resource provisioning, ensuring service continuity during mobility.