Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems

2021/08/01 by Xisuo Ma, Zhen Gao, Feifei Gao +1 · 14 citations

paper · doi:10.1109/jsac.2021.3087269

Cited by

• Reducing Pilots in Channel Estimation with Predictive Foundation Models
• Vision and Causal Learning Based Channel Estimation for THz Communications
• Transformer-Based Sparse CSI Estimation for Non-Stationary Channels
• Model-Free Channel Estimation for Massive MIMO: A Channel Charting-Inspired Approach
• Observation Matrix Design for Densifying MIMO Channel Estimation via 2D Ice Filling
• Filter-and-Attend: Wireless Channel Foundation Model with Noise-Plus-Interference Suppression Structure
• E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion
• MUSE-FM: Multi-task Environment-aware Foundation Model for Wireless Communications
• Unsupervised Deep Equilibrium Model Learning for Large-Scale Channel Estimation with Performance Guarantees
• Binary Hypothesis Testing-Based Low-Complexity Beamspace Channel Estimation for mmWave Massive MIMO Systems
• Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design
• Residual Cross-Attention Transformer-Based Multi-User CSI Feedback with Deep Joint Source-Channel Coding
• Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks
• Generative and Explainable AI for High-Dimensional Channel Estimation