9 citations · 13 across the 14 of their papers we have counts for
14 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…
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…
Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection
Zijian Cao, Hua Zhang, Le Liang +3
With the development of computer vision, 3D object detection has become increasingly important in many real-world applications. Limited by the computing power of sensor-side hardwa…
Hybrid Beamforming Design for Bistatic Integrated Sensing and Communication Systems
Tianhao Mao, Jie Yang, Le Liang +1
Integrated sensing and communication (ISAC) in millimeter wave is a key enabler for next-generation networks, which leverages large bandwidth and extensive antenna arrays, benefiti…
Fast Adaptation for Deep Learning-based Wireless Communications
Ouya Wang, Hengtao He, Shenglong Zhou +4
The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges h…
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…
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…
Semantic Satellite Communications Based on Generative Foundation Model
Peiwen Jiang, Chao-Kai Wen, Xiao Li +2
Satellite communications can provide massive connections and seamless coverage, but they also face several challenges, such as rain attenuation, long propagation delays, and co-cha…
Semantic Communication for Cooperative Perception based on Importance Map
Yucheng Sheng, Hao Ye, Le Liang +2
Cooperative perception, which has a broader perception field than single-vehicle perception, has played an increasingly important role in autonomous driving to conduct 3D object de…
Meta Reinforcement Learning for Fast Spectrum Sharing in Vehicular Networks
Kai Huang, Le Liang, Shi Jin +1
In this paper, we investigate the problem of fast spectrum sharing in vehicle-to-everything communication. In order to improve the spectrum efficiency of the whole system, the spec…
Deep Learning for MIMO Channel Estimation: Interpretation, Performance, and Comparison
Hu Qiang, Gao Feifei, Zhang Hao +2
Deep learning (DL) has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments. However, even with such…