A joint pilot optimization and channel estimation algorithm based on CBAM-CNN for multipath fading environments in underground coal mines
Fei Han, Chaoyang Liu, Yan Lei, Zhezhe Sun · 2026
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Description
Fei Han and colleagues tackled in 2026 a communications problem inside underground coal mines. Tunnel walls, rough surfaces, and coal dust scatter radio signals so badly that estimating the channel becomes unreliable. Their method designs the pilot signals and the estimator together inside one attention-equipped neural network, instead of optimizing each separately as older approaches do. Attention blocks learn which features and which frequencies deserve focus.
In simulations of harsh fading, the method cut estimation error by about 87 percent against the standard least squares approach. It beat a strong image-based rival by about 41 percent, nearing the best classical estimator at a fraction of its computing cost. Removing parts one at a time showed the pieces reinforce each other. The authors recommend real tunnel trials next.
Catalog record
| CREATOR | Fei Han, Chaoyang Liu, Yan Lei, Zhezhe Sun |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 98571 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |