DRIFT is a joint estimation-prediction framework using CNN or LSTM layers that reduces pilot overhead in LEO NTN uplink scenarios, claiming up to 12% spectral efficiency gain and under 200k MAC operations with robustness to mismatches.
Channel Aging-Aware LSTM- Based Channel Prediction for Satellite Communications,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces OW-SED paradigm and WOOT framework with deformable attention for detecting known and unseen sound events in open-world settings.
citing papers explorer
-
DRIFT: Joint Channel Estimation and Prediction Towards Pilotless 6G Non-Terrestrial Networks
DRIFT is a joint estimation-prediction framework using CNN or LSTM layers that reduces pilot overhead in LEO NTN uplink scenarios, claiming up to 12% spectral efficiency gain and under 200k MAC operations with robustness to mismatches.
-
Towards Open World Sound Event Detection
Introduces OW-SED paradigm and WOOT framework with deformable attention for detecting known and unseen sound events in open-world settings.