mlsanityData to 7 Oct 2026

Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios

arXiv 2406.08887 ↗First posted 13 Jun 2024eess.SP cs.AI cs.ITcited by 4

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Abstract (arXiv)

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system's spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 13 Jun 2024.

Builds on

11 of 30 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
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  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2018Deep Learning-Based Channel Estimationcited by 75
  6. 2023Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilotscited by 8
  7. 2021Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systemscited by 4
  8. 2020Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMOcited by 3
  9. 2022Deep Channel Prediction: A DNN Framework for Receiver Design in Time-Varying Fading Channelscited by 1
  10. 2023A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systemscited by 1
  11. 2024Learning-based Block-wise Planar Channel Estimation for Time-Varying MIMO OFDMcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channelscited by 3
  2. 2026SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimationcited by 1
  3. 2025Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimizationcited by 0
  4. 2026Deep Learning-Based Channel Extrapolation for Dual-Band Massive MIMO Systemscited by 0