# Helelena Ce Eval

> Evaluates deep learning architectures for pilot-based channel estimation in 5G-NR OFDM systems. It probes the model's ability to reconstruct full Channel State Information (CSI) from sparse pilot measurements across varying SNR levels, Doppler shifts, and 3GPP TDL propagation profiles. Use when the user wants to benchmark on 5G Deep Learning Data Synthesis (MATLAB), or asks about evaluating this task. Reports accuracy.

- Skill: `qhjqhj00/helelena-ce-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/helelena-ce-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/helelena-ce-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/helelena-ce-eval

---


# helelena-ce-eval

> HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention — Camelo Botero et al. (2025) (arXiv:2506.13408, 2025)

## What this evaluates

Evaluates deep learning architectures for pilot-based channel estimation in 5G-NR OFDM systems. It probes the model's ability to reconstruct full Channel State Information (CSI) from sparse pilot measurements across varying SNR levels, Doppler shifts, and 3GPP TDL propagation profiles.

## Datasets

- **5G Deep Learning Data Synthesis (MATLAB)** — total 11264; splits: train (-1), val (-1), test (-1)

## Metrics

- `accuracy` **(primary)** — range: dB
  - Mean Squared Error (MSE) or Normalized MSE expressed in decibels (10*log10(MSE)), where lower (more negative) values indicate better channel estimation performance.

## Input / output format

**Input**: LS estimates at pilot positions or LI-interpolated LS estimates.

**Output**: Full CSI (Channel State Information) estimates.

## Scoring recipe

```python
def compute_accuracy_dB(predictions, gold):
    mse = np.mean((predictions - gold) ** 2)
    return 10 * np.log10(mse)
```

## Common pitfalls

- Input preprocessing differs across methods: HELENA/LSiDNN use raw LS estimates at pilot positions, while baselines use LI-interpolated LS estimates, complicating direct accuracy comparisons.
- Model parameter count does not strictly dictate inference latency; lightweight dual-attention designs can achieve significantly faster inference than heavier ViT-based baselines despite comparable accuracy.

## Evidence (verbatim from paper)

> It achieves 45% faster inference (0.175 ms vs. 0.318 ms) and comparable accuracy (−16.78 dB vs. −17.30 dB) to CEViT while using 8× fewer parameters (0.11M vs. 0.88M), demonstrating superior accuracy-efficiency trade-offs without interpolation, and challenges the assumption that model complexity directly correlates with inference latency.

## Citation

```bibtex
@misc{camelobotero2025helenachannelestimation,
  title={HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention},
  author={Camelo Botero et al. (2025)},
  year={2025},
  note={arXiv:2506.13408}
}
```

- arXiv: 2506.13408