CDiT uses a diffusion transformer conditioned on position to generate high-fidelity THz channels in sparse beamspace under the hybrid planar-spherical wave model, outperforming benchmarks on realistic datasets.
Quadriga: A 3-d multi-cell channel model with time evolution for enabling virtual field trials
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Mode-tensorized CP decomposition reshapes MIMO channel tensors via virtual modes to improve path separability and provide denoising for better estimation accuracy at low SNR.
A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations.
citing papers explorer
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CDiT: Conditional Diffusion Transformer for Geometry-Aware Terahertz Cross Far- and Near-Field Channel Generation
CDiT uses a diffusion transformer conditioned on position to generate high-fidelity THz channels in sparse beamspace under the hybrid planar-spherical wave model, outperforming benchmarks on realistic datasets.
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Mode-Tensorized Canonical Polyadic Decomposition for MIMO Channel Estimation
Mode-tensorized CP decomposition reshapes MIMO channel tensors via virtual modes to improve path separability and provide denoising for better estimation accuracy at low SNR.
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ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture
A hybrid CNN-KAN model with dual-domain and multi-scale frequency enhancement predicts CSI more accurately than RNN, LSTM, GRU, CNN, and Transformer baselines on QuaDRiGa simulations.