# An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation

arXiv: https://arxiv.org/abs/2108.09430 · HTML: https://mlsanity.com/paper/2108.09430/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2021.

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

- [Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training](https://mlsanity.com/paper/2302.04368/) (2023)
- [Attention Based Neural Networks for Wireless Channel Estimation](https://mlsanity.com/paper/2204.13465/) (2022)
- [Robust Channel Estimation for Optical Wireless Communications Using Neural Network](https://mlsanity.com/paper/2504.02134/) (2025)
- [LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy](https://mlsanity.com/paper/2302.09409/) (2023)
- [Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems](https://mlsanity.com/paper/2306.06125/) (2023)
- [Achieving Robust Channel Estimation Neural Networks by Designed Training Data](https://mlsanity.com/paper/2507.12630/) (2025)
- [Learn to Adapt to New Environment from Past Experience and Few Pilot](https://mlsanity.com/paper/2209.02649/) (2022)
- [LLM4XCE: Large Language Models for Extremely Large-Scale Massive MIMO Channel Estimation](https://mlsanity.com/paper/2512.08955/) (2025)
- [Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO](https://mlsanity.com/paper/2605.15032/) (2026)
- [Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation](https://mlsanity.com/paper/2608.14676/) (2026)

## Abstract (arXiv)

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accuracy of highly separable channels with narrow angular spread by realizing the "divide-and-conquer" policy. Specifically, we introduce a novel attention-aided DL channel estimation framework for conventional massive MIMO systems and devise an embedding method to effectively integrate the attention mechanism into the fully connected neural network for the hybrid analog-digital (HAD) architecture. Simulation results show that in both scenarios, the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead. Furthermore, strong robustness under different system and channel parameters can be achieved by the proposed approach, which further strengthens its practical value. We also investigate the distributions of learned attention maps to reveal the role of attention, which endows the proposed approach with a certain degree of interpretability.