13 papers · 1 filter
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…
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…
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…
ISAC Prototype System for Multi-Domain Cooperative Communication Networks
Jie Yang, Hang Que, Tao Du +4
Future wireless networks are poised to transform into integrated sensing and communication (ISAC) networks, unlocking groundbreaking services such as digital twinning. To harness t…
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…
Generative Diffusion Models for High Dimensional Channel Estimation
Xingyu Zhou, Le Liang, Jing Zhang +3
Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by th…
Mini-Batch Gradient-Based MCMC for Decentralized Massive MIMO Detection
Xingyu Zhou, Le Liang, Jing Zhang +2
Massive multiple-input multiple-output (MIMO) technology has significantly enhanced spectral and power efficiency in cellular communications and is expected to further evolve towar…
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…
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…