Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2511.00225 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Inhaltsangabe:
- Channel estimation is fundamental to wireless communications, yet it becomes increasingly challenging in massive multiple-input multiple-output (MIMO) systems where base stations employ hundreds of antennas. Traditional least-squares methods require prohibitive pilot overhead that scales with antenna count, while sparse estimation methods depend on precise channel models that may not always be practical. This paper proposes a model-free approach combining deep autoencoders and LSTM networks. The method first learns low-dimensional channel representations preserving temporal correlation through augmenting a channel charting-inspired loss function, then tracks these features to recover full channel information from limited pilots. Simulation results using ray-tracing datasets show that the proposed approach achieves up to 9 dB improvement in normalized mean square error compared to the least-squares methods under ill-conditioned scenarios, while maintaining scalability across MIMO configurations.