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.
Role and Evolution of Non- Terrestrial Networks Toward 6G Systems,
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A preconditioning technique for the shifted Helmholtz operator stabilizes EFIE iterative solvers across multiple frequency and discretization regimes, enabling quasi-linear complexity.
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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.
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High-Frequency Preconditioners for Electromagnetic Integral Equations Based on Helmholtz Regularizations
A preconditioning technique for the shifted Helmholtz operator stabilizes EFIE iterative solvers across multiple frequency and discretization regimes, enabling quasi-linear complexity.