# CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback

arXiv: https://arxiv.org/abs/2102.07507 · HTML: https://mlsanity.com/paper/2102.07507/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://mlsanity.com/paper/1906.06007/) (2019)
- [Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://mlsanity.com/paper/1910.14322/) (2019)
- [Bit-level Optimized Neural Network for Multi-antenna Channel Quantization](https://mlsanity.com/paper/1909.10730/) (2019)
- [Binary Neural Network Aided CSI Feedback in Massive MIMO System](https://mlsanity.com/paper/2011.02692/) (2020)

## Built on by (most cited first, 15 of 15)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach](https://mlsanity.com/paper/2401.09680/) (2024)
- [Quantization Adaptor for Bit-Level Deep Learning-Based Massive MIMO CSI Feedback](https://mlsanity.com/paper/2211.02937/) (2022)
- [Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving](https://mlsanity.com/paper/2402.05146/) (2024)
- [Enhancing Deep Learning Performance of Massive MIMO CSI Feedback](https://mlsanity.com/paper/2208.11333/) (2022)
- [Deep Learning Empowered Type-II Codebook: New Paradigm for Enhancing CSI Feedback](https://mlsanity.com/paper/2305.08081/) (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)
- [Dig-CSI: A Distributed and Generative Model Assisted CSI Feedback Training Framework](https://mlsanity.com/paper/2312.05921/) (2023)
- [An Efficient Network with Novel Quantization Designed for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2405.20068/) (2024)
- [Self-information Domain-based Neural CSI Compression with Feature Coupling](https://mlsanity.com/paper/2305.07662/) (2023)
- [Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback](https://mlsanity.com/paper/2403.08133/) (2024)
- [Information-Preserving CSI Feedback: Invertible Networks with Endogenous Quantization and Channel Error Mitigation](https://mlsanity.com/paper/2507.20283/) (2025)
- [GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels](https://mlsanity.com/paper/2608.18522/) (2026)
- [DiffSAC: Diffusion-guided Sampling for Consensus-based Robust Estimation](https://mlsanity.com/paper/2608.30603/) (2026)

## Abstract (arXiv)

Unleashing the full potential of massive MIMO in FDD mode by reducing the overhead of CSI feedback has recently garnered attention. Numerous deep learning for massive MIMO CSI feedback approaches have demonstrated their efficiency and potential. However, most existing methods improve accuracy at the cost of computational complexity and the accuracy decreases significantly as the CSI compression rate increases. This paper presents a novel neural network CLNet tailored for CSI feedback problem based on the intrinsic properties of CSI. CLNet proposes a forge complex-valued input layer to process signals and utilizes attention mechanism to enhance the performance of the network. The experiment result shows that CLNet outperforms the state-of-the-art method by average accuracy improvement of 5.41% in both outdoor and indoor scenarios with average 24.1% less computational overhead. Codes for deep learning-based CSI feedback CLNet are available at GitHub.