14 papers
Low-Overhead Receiver Design for Data-Dependent Superimposed Training via Deep Learning
Xinjie Li, Xingyu Zhou, Jing Zhang +3
Superimposed pilot (SIP) transmission improves spectral efficiency by eliminating the dedicated pilot overhead required in orthogonal pilot (OP)-based schemes. However, SIP suffers…
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…
Improving Channel Estimation via Multimodal Diffusion Models with Flow Matching
Xiaotian Fan, Xingyu Zhou, Le Liang +2
Deep generative models offer a powerful alternative to conventional channel estimation by learning complex channel distributions. By integrating the rich environmental information…
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…
Super-Resolution ISAC Receivers: An MCMC-Based Gridless Sparse Bayesian Learning Approach
Keying Zhu, Xingyu Zhou, Jie Yang +2
Integrated sensing and communication (ISAC) is crucial for low-altitude wireless networks (LAWNs), where the safety-critical demand for high-accuracy sensing creates a trade-off be…
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…