# Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems

arXiv: https://arxiv.org/abs/2106.04043 · HTML: https://mlsanity.com/paper/2106.04043/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2021.

## Built on by (most cited first, 10 of 10)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT](https://mlsanity.com/paper/2110.14937/) (2021)
- [RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing](https://mlsanity.com/paper/2402.04888/) (2024)
- [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)
- [An Efficient Network with Novel Quantization Designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2405.20068/) (2024)
- [TinySense: Effective CSI Compression for Scalable and Accurate Wi-Fi Sensing](https://mlsanity.com/paper/2601.15838/) (2026)
- [Better Lightweight Network for Free: Codeword Mimic Learning for Massive MIMO CSI feedback](https://mlsanity.com/paper/2210.16544/) (2022)
- [Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO](https://mlsanity.com/paper/2211.15079/) (2022)
- [Distributed Gossip-GAN for Low-overhead CSI Feedback Training in FDD mMIMO-OFDM Systems](https://mlsanity.com/paper/2509.10490/) (2025)

## Abstract (arXiv)

Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlink channel state information (CSI) from users to the base station (BS), in order to fulfill the precoding design at the BS. However, the large dimension of CSI matrices in the massive MIMO system makes the CSI feedback very challenging, and it is urgent to compress the feedback CSI. To this end, this paper proposes a novel dilated convolution based CSI feedback network, namely DCRNet. Specifically, the dilated convolutions are used to enhance the receptive field (RF) of the proposed DCRNet without increasing the convolution size. Moreover, advanced encoder and decoder blocks are designed to improve the reconstruction performance and reduce computational complexity as well. Numerical results are presented to show the superiority of the proposed DCRNet over the conventional networks. In particular, the proposed DCRNet can achieve almost the state-of-the-arts (SOTA) performance with much lower floating point operations (FLOPs). The open source code and checkpoint of this work are available at https://github.com/recusant7/DCRNet.