# Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems arXiv: https://arxiv.org/abs/2104.11052 ยท HTML: https://mlsanity.com/paper/2104.11052/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 17 of its 44 references that are in the map. - First posted on arXiv 22 Apr 2021. ## Builds on (most cited first, 17 of 17) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Model-Driven Deep Learning for Physical Layer Communications](https://mlsanity.com/paper/1809.06059/) (2018) - [Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?](https://mlsanity.com/paper/1902.02647/) (2019) - [Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://mlsanity.com/paper/1906.06007/) (2019) - [Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions](https://mlsanity.com/paper/1904.09673/) (2019) - [Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO](https://mlsanity.com/paper/2007.06512/) (2020) - [Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO](https://mlsanity.com/paper/1906.09867/) (2019) - [Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding](https://mlsanity.com/paper/1901.06537/) (2019) - [Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems](https://mlsanity.com/paper/2004.07576/) (2020) - [Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO](https://mlsanity.com/paper/2003.05875/) (2020) - [Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access](https://mlsanity.com/paper/2008.01992/) (2020) - [High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO System](https://mlsanity.com/paper/2002.06377/) (2020) - [Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO](https://mlsanity.com/paper/2002.06380/) (2020) - [20 Years of Evolution from Cognitive to Intelligent Communications](https://mlsanity.com/paper/1909.11562/) (2019) - [Deep Learning Based FDD Non-Stationary Massive MIMO Downlink Channel Reconstruction](https://mlsanity.com/paper/2002.09858/) (2020) - [Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results](https://mlsanity.com/paper/2006.08896/) (2020) - [Compressive Sensing Based Channel Estimation for Millimeter-Wave Full-Dimensional MIMO with Lens-Array](https://mlsanity.com/paper/1912.10668/) (2019) ## Built on by (most cited first, 12 of 12) - [Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication](https://mlsanity.com/paper/2205.03770/) (2022) - [Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI](https://mlsanity.com/paper/2201.06778/) (2022) - [CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels](https://mlsanity.com/paper/2406.03438/) (2024) - [Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO](https://mlsanity.com/paper/2209.08456/) (2022) - [Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network](https://mlsanity.com/paper/2405.19889/) (2024) - [Hybrid Knowledge-Data Driven Channel Semantic Acquisition and Beamforming for Cell-Free Massive MIMO](https://mlsanity.com/paper/2307.03070/) (2023) - [Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect](https://mlsanity.com/paper/2403.12813/) (2024) - [Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems](https://mlsanity.com/paper/2302.02401/) (2023) - [Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2311.15950/) (2023) - [Residual Cross-Attention Transformer-Based Multi-User CSI Feedback with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2505.19465/) (2025) - [Multi-beam Beamforming in RIS-aided MIMO Subject to Reradiation Mask Constraints -- Optimization and Machine Learning Design](https://mlsanity.com/paper/2507.15367/) (2025) - [E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion](https://mlsanity.com/paper/2509.19312/) (2025) ## Abstract (arXiv) This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels' sparsity is exploited for reducing the overhead. Firstly, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.