# Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems

arXiv: https://arxiv.org/abs/2306.06125 · HTML: https://mlsanity.com/paper/2306.06125/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [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)
- [Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2003.04684/) (2020)
- [Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems](https://mlsanity.com/paper/2006.11796/) (2020)
- [A Speculative Study on 6G](https://mlsanity.com/paper/1902.06700/) (2019)
- [Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2005.00438/) (2020)
- [Deep Learning for Joint Channel Estimation and Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2011.07242/) (2020)
- [Data-Driven Deep Learning to Design Pilot and Channel Estimator For Massive MIMO](https://mlsanity.com/paper/2003.05875/) (2020)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [Attention Based Neural Networks for Wireless Channel Estimation](https://mlsanity.com/paper/2204.13465/) (2022)
- [Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems](https://mlsanity.com/paper/2106.04043/) (2021)
- [An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation](https://mlsanity.com/paper/2108.09430/) (2021)
- [AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems](https://mlsanity.com/paper/2008.07112/) (2020)
- [AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback](https://mlsanity.com/paper/2206.07949/) (2022)
- [AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback](https://mlsanity.com/paper/2106.06759/) (2021)

## Built on by (most cited first, 1 of 1)

- [Geometric Inductive Biases for Semi-Supervised Equalization: The Constellation-Aware Transformer](https://mlsanity.com/paper/2609.33695/) (2026)

## Abstract (arXiv)

The downlink channel state information (CSI) estimation and low overhead acquisition are the major challenges for massive MIMO systems in frequency division duplex to enable high MIMO gain. Recently, numerous studies have been conducted to harness the power of deep neural networks for better channel estimation and feedback. However, existing methods have yet to fully exploit the intrinsic correlation features present in CSI. As a consequence, distinct network structures are utilized for handling these two tasks separately. To achieve joint channel estimation and feedback, this paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. To effectively capture and restructure correlation features, a self-mask-attention coding is proposed, complemented by an active masking strategy designed to improve efficiency. The channel estimation is achieved through the decoder part, wherein a lightweight multilayer perceptron denoising module is utilized for further accurate estimation. Extensive experiments demonstrate that our method not only outperforms state-of-the-art channel estimation and feedback techniques in joint tasks but also achieves beneficial performance in individual tasks.