7 papers
A Graph Foundation Model for Wireless Resource Allocation
Yucheng Sheng, Jiacheng Wang, Le Liang +2
The aggressive densification of modern wireless networks necessitates judicious resource allocation to mitigate severe mutual interference. However, classical iterative algorithms…
Wireless Power Control Based on Large Language Models
Jiacheng Wang, Yucheng Sheng, Le Liang +2
This paper investigates the power control problem in wireless networks by repurposing pre-trained large language models (LLMs) as relational reasoning backbones. In hyper-connected…
Large Language Models for Wireless Communications: From Adaptation to Autonomy
Le Liang, Hao Ye, Yucheng Sheng +4
The emergence of large language models (LLMs) has revolutionized artificial intelligence, offering unprecedented capabilities in reasoning, generalization, and zero-shot learning.…
A Wireless Foundation Model for Multi-Task Prediction
Yucheng Sheng, Jiacheng Wang, Xingyu Zhou +4
With the growing complexity and dynamics of the mobile communication networks, accurately predicting key system parameters, such as channel state information (CSI), user location,…
SComCP: Task-Oriented Semantic Communication for Collaborative Perception
Jipeng Gan, Yucheng Sheng, Hua Zhang +4
Reliable detection of surrounding objects is critical for the safe operation of connected automated vehicles (CAVs). However, inherent limitations such as the restricted perception…
Beam Prediction based on Large Language Models
Yucheng Sheng, Kai Huang, Le Liang +3
In this letter, we use large language models (LLMs) to develop a high-performing and robust beam prediction method. We formulate the millimeter wave (mmWave) beam prediction proble…