11 papers · 1 filter
Reducing Pilots in Channel Estimation with Predictive Foundation Models
Xingyu Zhou, Le Liang, Hao Ye +3
Accurate channel state information (CSI) acquisition is essential for modern wireless systems, which becomes increasingly difficult under large antenna arrays, strict pilot overhea…
Cross-Modal Semantic Communication for Heterogeneous Collaborative Perception
Mingyi Lu, Guowei Liu, Le Liang +3
Collaborative perception, an emerging paradigm in autonomous driving, has been introduced to mitigate the limitations of single-vehicle systems, such as limited sensor range and oc…
Multimodal-Wireless: A Large-Scale Dataset for Sensing and Communication
Tianhao Mao, Le Liang, Jie Yang +3
This paper presents Multimodal-Wireless, a large-scale open-source dataset for multimodal sensing and communication research. The dataset is generated through an integrated and cus…
RSU-Assisted Resource Allocation for Collaborative Perception
Guowei Liu, Le Liang, Chongtao Guo +2
As a pivotal technology for autonomous driving, collaborative perception enables vehicular agents to exchange perceptual data through vehicle-to-everything (V2X) communications, th…
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,…
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.…
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…
Hierarchical Multi-Agent Reinforcement Learning-based Coordinated Spatial Reuse for Next Generation WLANs
Jiaming Yu, Le Liang, Hao Ye +1
High-density Wi-Fi deployments often result in significant co-channel interference, which degrades overall network performance. To address this issue, coordination of multi access…
Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach
Hao Fang, Kai Huang, Hao Ye +4
The pursuit of rate maximization in wireless communication frequently encounters substantial challenges associated with user fairness. This paper addresses these challenges by expl…
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…