# Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis

arXiv: https://arxiv.org/abs/1906.06007 · HTML: https://mlsanity.com/paper/1906.06007/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

## Built on by (most cited first, 30 of 35)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Generative AI for Physical Layer Communications: A Survey](https://mlsanity.com/paper/2312.05594/) (2023)
- [A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning](https://mlsanity.com/paper/2505.09160/) (2025)
- [CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2102.07507/) (2021)
- [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)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach](https://mlsanity.com/paper/2106.12556/) (2021)
- [Changeable Rate and Novel Quantization for CSI Feedback Based on Deep Learning](https://mlsanity.com/paper/2202.13627/) (2022)
- [Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots](https://mlsanity.com/paper/2303.00986/) (2023)
- [CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning](https://mlsanity.com/paper/2101.04377/) (2021)
- [Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2105.00354/) (2021)
- [Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback](https://mlsanity.com/paper/2211.02937/) (2022)
- [Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2403.07355/) (2024)
- [Generative Diffusion Model-based Compression of MIMO CSI](https://mlsanity.com/paper/2503.03753/) (2025)
- [Convolutional Autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless Systems](https://mlsanity.com/paper/2110.12348/) (2021)
- [Aggregated Network for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2101.06618/) (2021)
- [Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems](https://mlsanity.com/paper/2103.00125/) (2021)
- [Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback](https://mlsanity.com/paper/2305.08081/) (2023)
- [Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems](https://mlsanity.com/paper/2306.06125/) (2023)
- [Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2311.15950/) (2023)
- [Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach](https://mlsanity.com/paper/2312.02184/) (2023)
- [A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems](https://mlsanity.com/paper/2312.04062/) (2023)
- [An Efficient Network with Novel Quantization Designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2405.20068/) (2024)
- [An Adaptive CSI Feedback Model Based on BiLSTM for Massive MIMO-OFDM Systems](https://mlsanity.com/paper/2408.06359/) (2024)
- [Demonstrating Interoperable Channel State Feedback Compression with Machine Learning](https://mlsanity.com/paper/2506.21796/) (2025)
- [Wireless Dataset Similarity: Measuring Distances in Supervised and Unsupervised Machine Learning](https://mlsanity.com/paper/2601.01023/) (2026)
- [Scalable Deep Compressive Sensing](https://mlsanity.com/paper/2101.08024/) (2021)
- [Tensor Learning-based Precoder Codebooks for FD-MIMO Systems](https://mlsanity.com/paper/2106.11374/) (2021)
- [Deep Learning for 1-Bit Compressed Sensing-based Superimposed CSI Feedback](https://mlsanity.com/paper/2203.06606/) (2022)

## Abstract (arXiv)

Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy. Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.