5 papers
Sim2Field: End-to-End Development of AI RANs for 6G
Russell Ford, Hao Chen, Pranav Madadi +17
Following state-of-the-art research results, which showed the potential for significant performance gains by applying AI/ML techniques in the cellular Radio Access Network (RAN), t…
MoE-CE: Enhancing Generalization for Deep Learning based Channel Estimation via a Mixture-of-Experts Framework
Tianyu Li, Yan Xin, Jianzhong +1
Reliable channel estimation (CE) is fundamental for robust communication in dynamic wireless environments, where models must generalize across varying conditions such as signal-to-…
Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management
Yuyan Lin, Hao Zhou, Chengming Hu +5
6G networks have become increasingly complicated due to novel network architecture and newly emerging signal processing and transmission techniques, leading to significant burdens…
Understanding 6G through Language Models: A Case Study on LLM-aided Structured Entity Extraction in Telecom Domain
Ye Yuan, Haolun Wu, Hao Zhou +5
Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extracti…
Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation
Dun Yuan, Hao Zhou, Di Wu +5
Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks. However, LLMs are still facing challenges when applied to domain-s…