16 papers
A Graph Foundation Model for Large-Scale MIMO Detection
Xingyu Zhou, Le Liang, Hao Ye +5
Large-scale multiple-input multiple-output (MIMO) detection is fundamental to modern wireless networks but constrained by performance-complexity trade-offs. Existing detectors, whe…
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…
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…
Semantic Communication for Cooperative Perception using HARQ
Yucheng Sheng, Le Liang, Hao Ye +2
Cooperative perception, offering a wider field of view than standalone perception, is becoming increasingly crucial in autonomous driving. This perception is enabled through vehicl…
Meta-Learning Empowered Graph Neural Networks for Radio Resource Management
Kai Huang, Le Liang, Xinping Yi +3
In this paper, we consider a radio resource management (RRM) problem in the dynamic wireless networks, comprising multiple communication links that share the same spectrum resource…
On Purely Data-Driven Massive MIMO Detectors
Hao Ye, Le Liang
The development of learning-based detectors for massive multi-input multi-output (MIMO) systems has been hindered by the inherent complexities arising from the problem's high dimen…