7 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…
Deep Learning-based Low-Overhead Beam Alignment for mmWave Massive MIMO Systems
Weijie Jin, Jing Zhang, Hengtao He +3
Millimeter-wave massive multiple-input multiple-output systems employ highly directional beamforming to overcome severe path loss, and their performance critically depends on accur…
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…
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…
Learning-Aided Iterative Receiver for Superimposed Pilots: Design and Experimental Evaluation
Xinjie Li, Xingyu Zhou, Yixiao Cao +4
The superimposed pilot transmission scheme offers substantial potential for improving spectral efficiency in MIMO-OFDM systems, but it presents significant challenges for receiver…