# Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO

arXiv: https://arxiv.org/abs/2007.06512 · HTML: https://mlsanity.com/paper/2007.06512/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 26 of 26)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Generating High Dimensional User-Specific Wireless Channels using Diffusion Models](https://mlsanity.com/paper/2409.03924/) (2024)
- [Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems](https://mlsanity.com/paper/2104.11052/) (2021)
- [Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI](https://mlsanity.com/paper/2201.06778/) (2022)
- [Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation](https://mlsanity.com/paper/2108.01529/) (2021)
- [Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation](https://mlsanity.com/paper/2209.15347/) (2022)
- [Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2105.00354/) (2021)
- [Learn to Communicate with Neural Calibration: Scalability and Generalization](https://mlsanity.com/paper/2110.00272/) (2021)
- [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)
- [Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding](https://mlsanity.com/paper/2110.12059/) (2021)
- [A Model-based GNN for Learning Precoding](https://mlsanity.com/paper/2212.00860/) (2022)
- [Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding](https://mlsanity.com/paper/2209.10332/) (2022)
- [Precoding-oriented Massive MIMO CSI Feedback Design](https://mlsanity.com/paper/2302.11526/) (2023)
- [A Semi-amortized Lifted Learning-to-Optimize Masked (SALLO-M) Transformer Model for Scalable and Generalizable Beamforming](https://mlsanity.com/paper/2510.13077/) (2025)
- [Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback](https://mlsanity.com/paper/2209.01436/) (2022)
- [Towards Efficient Subarray Hybrid Beamforming: Attention Network-based Practical Feedback in FDD Massive MU-MIMO Systems](https://mlsanity.com/paper/2302.02401/) (2023)
- [Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback](https://mlsanity.com/paper/2305.08081/) (2023)
- [Downlink Channel Covariance Matrix Estimation via Representation Learning with Graph Regularization](https://mlsanity.com/paper/2407.18865/) (2024)
- [Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems](https://mlsanity.com/paper/2302.07601/) (2023)
- [Uplink Assisted Joint Channel Estimation and CSI Feedback: An Approach Based on Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2504.10836/) (2025)
- [E2E Learning Massive MIMO for Multimodal Semantic Non-Orthogonal Transmission and Fusion](https://mlsanity.com/paper/2509.19312/) (2025)
- [Precoding-Oriented CSI Feedback Design with Mutual Information Regularized VQ-VAE](https://mlsanity.com/paper/2602.02508/) (2026)
- [Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems](https://mlsanity.com/paper/2607.11970/) (2026)

## Abstract (arXiv)

This paper shows that deep neural network (DNN) can be used for efficient and distributed channel estimation, quantization, feedback, and downlink multiuser precoding for a frequency-division duplex massive multiple-input multiple-output system in which a base station (BS) serves multiple mobile users, but with rate-limited feedback from the users to the BS. A key observation is that the multiuser channel estimation and feedback problem can be thought of as a distributed source coding problem. In contrast to the traditional approach where the channel state information (CSI) is estimated and quantized at each user independently, this paper shows that a joint design of pilots and a new DNN architecture, which maps the received pilots directly into feedback bits at the user side then maps the feedback bits from all the users directly into the precoding matrix at the BS, can significantly improve the overall performance. This paper further proposes robust design strategies with respect to channel parameters and also a generalizable DNN architecture for varying number of users and number of feedback bits. Numerical results show that the DNN-based approach with short pilot sequences and very limited feedback overhead can already approach the performance of conventional linear precoding schemes with full CSI.