# HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention

arXiv: https://arxiv.org/abs/2506.13408 · HTML: https://mlsanity.com/paper/2506.13408/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [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)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [Low Complexity Deep Learning Augmented Wireless Channel Estimation for Pilot-Based OFDM on Zynq System on Chip](https://mlsanity.com/paper/2403.01098/) (2024)

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

- [HELENA for 5G NR LEO NTN Channel Estimation: A Comparative Evaluation](https://mlsanity.com/paper/2609.14735/) (2026)

## Abstract (arXiv)

Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight convolutional backbone with two efficient attention mechanisms: patch-wise multi-head self-attention for capturing global dependencies and a squeeze-and-excitation block for local feature refinement. Compared to CEViT, a state-of-the-art vision transformer-based estimator, HELENA reduces inference time by 45.0% (0.175,ms vs. 0.318,ms), achieves comparable accuracy (-16.78,dB vs. -17.30,dB), and requires 8× fewer parameters (0.11M vs. 0.88M), demonstrating its suitability for low-latency, real-time deployment.