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This paper studies inverse sampling for Lévy-driven generative models, proposing a structured reverse sampler that decomposes dynamics into diffusion, small jump, and large jump components, with neural networks amortizing jump rates. The method is applied to OFDM-SISO channel estimation under mixed Gaussian and impulsive noise.
Proposes a message-passing-based two-timescale Bayesian deep learning framework for joint channel and memory hardware impairment tracking in massive MIMO systems.
PilotWiMAE introduces a self-supervised framework that directly ingests noisy pilot observations for wireless channel representation learning, removing the unrealistic full-CSI assumption and enabling robust cross-frequency beam selection and channel estimation that beats supervised baselines.