LITE combines a 1D conv autoencoder for 50% CSI compression with an asymmetric SE-BiLSTM predictor to cut model complexity 83% and X-haul load while losing only 6% accuracy versus a full BiLSTM baseline.
Model-driven deep learning based precoding for fdd cell-free massive mimo with imperfect csi,
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LITE: Lightweight Channel Gain Estimation with Reduced X-Haul CSI Signaling in O-RAN
LITE combines a 1D conv autoencoder for 50% CSI compression with an asymmetric SE-BiLSTM predictor to cut model complexity 83% and X-haul load while losing only 6% accuracy versus a full BiLSTM baseline.