6g

This paper proposes a randomized anchoring strategy to mitigate anchoring bias in LLM-based agents for energy-efficient 6G autonomous networks, achieving up to 25% energy savings using a lightweight 1B-parameter model.

This paper presents an open-source NWDAF compatible with Free5GC that integrates an LLM interface for natural language interaction and intent-based network management, aiming toward AI-native 6G networks.

PilotWiMAE introduces a self-supervised framework that directly ingests noisy pilot observations for wireless channel representation learning, removing the unrealistic full-CSI assumption and enabling robust cross-frequency beam selection and channel estimation that beats supervised baselines.

This paper presents a visionary framework for AI-native 6G networks, proposing a unified foundation model and collaborative multi-agent systems to achieve autonomous, resilient network management beyond fragmented 5G approaches.