A gated-fusion CSI predictor using GRU, attention, and DSLH reaches -13.84 dB NMSE with 26% fewer parameters and 2.3x higher throughput than a LinFormer baseline on 3GPP channels.
Channe l prediction using deep recurrent neural network with evt-based adaptiv e quantile loss function
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Resource-Efficient CSI Prediction: A Gated Fusion and Factorized Projection Approach
A gated-fusion CSI predictor using GRU, attention, and DSLH reaches -13.84 dB NMSE with 26% fewer parameters and 2.3x higher throughput than a LinFormer baseline on 3GPP channels.