6 papers · 1 filter
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…
Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching
Xiaotian Fan, Xingyu Zhou, Le Liang +2
Deep generative models offer a powerful alternative to conventional channel estimation by learning complex channel distributions. By integrating the rich environmental information…
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
Jiaming Yu, Le Liang, Chongtao Guo +3
This paper investigates the use of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks. In particular, we consider the c…
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning
Jiacheng Wang, Le Liang, Hao Ye +2
Judicious resource allocation can effectively enhance federated learning (FL) training performance in wireless networks by addressing both system and statistical heterogeneity. How…
Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception
Yandi Liu, Guowei Liu, Le Liang +3
Stand-alone perception systems in autonomous driving suffer from limited sensing ranges and occlusions at extended distances, potentially resulting in catastrophic outcomes. To add…
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…