# Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect

arXiv: https://arxiv.org/abs/2403.12813 · HTML: https://mlsanity.com/paper/2403.12813/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 68 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018)
- [Artificial Intelligence-Enabled Intelligent 6G Networks](https://mlsanity.com/paper/1912.05744/) (2019)
- [Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays](https://mlsanity.com/paper/1902.07678/) (2019)
- [Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication](https://mlsanity.com/paper/2205.03770/) (2022)
- [Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO](https://mlsanity.com/paper/1906.09867/) (2019)
- [Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO](https://mlsanity.com/paper/2003.05875/) (2020)
- [Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems](https://mlsanity.com/paper/2104.11052/) (2021)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN](https://mlsanity.com/paper/2006.11435/) (2020)
- [AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems](https://mlsanity.com/paper/2008.07112/) (2020)
- [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021)
- [Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information](https://mlsanity.com/paper/2103.00432/) (2021)
- [Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G](https://mlsanity.com/paper/2107.03059/) (2021)
- [Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?](https://mlsanity.com/paper/2109.07883/) (2021)
- [Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning](https://mlsanity.com/paper/2107.01001/) (2021)
- [Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems](https://mlsanity.com/paper/2107.11958/) (2021)
- [Near-Field Wideband Beamforming for Extremely Large Antenna Arrays](https://mlsanity.com/paper/2109.10054/) (2021)
- [Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems](https://mlsanity.com/paper/2104.13656/) (2021)
- [Sensing User's Channel and Location with Terahertz Extra-Large Reconfigurable Intelligent Surface under Hybrid-Field Beam Squint Effect](https://mlsanity.com/paper/2305.07184/) (2023)
- [Impact of Low-Resolution ADC on DOA Estimation Performance for Massive MIMO Receive Array](https://mlsanity.com/paper/2011.00451/) (2020)
- [Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications](https://mlsanity.com/paper/2305.19005/) (2023)

## Built on by (most cited first, 2 of 2)

- [Residual Cross-Attention Transformer-Based Multi-User CSI Feedback with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2505.19465/) (2025)
- [Near-Field Wideband Channel Estimation for XL-MIMO Systems via Denoising Diffusion Model](https://mlsanity.com/paper/2604.20494/) (2026)

## Abstract (arXiv)

Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UMMIMO) systems, due to the high-dimensional channel matrices, hybrid near- and far- field channel feature, beam squint effects, and imperfect hardware constraints, such as low-resolution analog-to-digital converters, and in-phase and quadrature imbalance. To overcome these challenges, this paper proposes an efficient downlink channel estimation (CE) and CSI feedback approach based on knowledge and data dual-driven deep learning (DL) networks. Specifically, we first propose a data-driven residual neural network de-quantizer (ResNet-DQ) to pre-process the received pilot signals at user equipment (UEs), where the noise and distortion brought by imperfect hardware can be mitigated. A knowledge-driven generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) network is then developed to jointly estimate the channels by exploiting the approximately same physical angle shared by different subcarriers. In particular, two wideband redundant dictionaries (WRDs) are proposed such that the measurement matrices of the GMMV-LAMP network can accommodate the far-field and near-field beam squint effect, respectively. Finally, we propose an encoder at the UEs and a decoder at the AP by a data-driven CSI residual network (CSI-ResNet) to compress the CSI matrix into a low-dimensional quantized bit vector for feedback, thereby reducing the feedback overhead substantially. Simulation results show that the proposed knowledge and data dual-driven approach outperforms conventional downlink CE and CSI feedback methods, especially in the case of low signal-to-noise ratios.