16 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…
AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models
Chengxuan Lu, Shukuan Wang, Yanjie Li +10
Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models is severely bottlenecked by synchronization barriers and the high cost of environment data acquisiti…
CoDS: Collaborative Perception via Digital Semantic Communication
Jipeng Gan, Le Liang, Hua Zhang +2
Semantic communication has been introduced into collaborative perception systems for autonomous driving, offering a promising approach to enhancing data transmission efficiency and…
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…
Conditional Diffusion Model-Enabled Scenario-Specific Neural Receivers for Superimposed Pilot Schemes
Xingyu Zhou, Le Liang, Xinjie Li +4
Neural receivers have demonstrated strong performance in wireless communication systems. However, their effectiveness typically depends on access to large-scale, scenario-specific…
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…
Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Models
Ziqi Diao, Xingyu Zhou, Le Liang +1
Channel estimation for massive multiple-input multiple-output (MIMO) systems is fundamentally constrained by excessive pilot overhead and high estimation latency. To overcome these…
Low-Complexity MIMO Channel Estimation with Latent Diffusion Models
Xiaotian Fan, Xingyu Zhou, Le Liang +1
Deep generative models offer a powerful alternative to conventional channel estimation by learning the complex prior distribution of wireless channels. Capitalizing on this potenti…
Next-Generation AI-Native Wireless Communications: MCMC-Based Receiver Architectures for Unified Processing
Xingyu Zhou, Le Liang, Jing Zhang +2
The multiple-input multiple-output (MIMO) receiver processing is a key technology for current and next-generation wireless communications. However, it faces significant challenges…
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…
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…
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…