3 citations · 7 across the 9 of their papers we have counts for
9 papers
Prompting Wireless Networks: Reinforced In-Context Learning for Power Control
Hao Zhou, Chengming Hu, Dun Yuan +5
To manage and optimize constantly evolving wireless networks, existing machine learning (ML)- based studies operate as black-box models, leading to increased computational costs du…
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…
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…
Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspective
Hao Zhou, Chengming Hu, Dun Yuan +5
Recently, large language models (LLMs) have been successfully applied to many fields, showing outstanding comprehension and reasoning capabilities. Despite their great potential, L…
Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving
Zijiang Yan, Hao Zhou, Hina Tabassum +1
Large language models (LLMs) have received considerable interest recently due to their outstanding reasoning and comprehension capabilities. This work explores applying LLMs to veh…
Self-Refined Generative Foundation Models for Wireless Traffic Prediction
Chengming Hu, Hao Zhou, Di Wu +3
With a broad range of emerging applications in 6G networks, wireless traffic prediction has become a critical component of network management. However, the dynamically shifting dis…
Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning
Hao Zhou, Chengming Hu, Dun Yuan +5
Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large language models (LLMs) have attracted consider…
Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control
Hao Zhou, Chengming Hu, Dun Yuan +4
Large language model (LLM) has recently been considered a promising technique for many fields. This work explores LLM-based wireless network optimization via in-context learning. T…
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models
Hao Zhou, Chengming Hu, Xue Liu
Reconfigurable intelligent surface (RIS) becomes a promising technique for 6G networks by reshaping signal propagation in smart radio environments. However, it also leads to signif…