Model Refinement Learning and an Example on Channel Estimation with Universal Noise Model

Model-based method and data-based method are two basic approaches for the design of wireless communication systems. Model-based methods suffer from inaccurate modeling assumptions due to excessively complex environment. Recently, data-based methods have achieved remarkable performances in the communication system design without the knowledge of accurate model but encounter some challenges such as, lack of available labelled training data and explainability. In this paper, we propose a novel hybrid idea to integrate the strengths of both data and model-based methods, named model refinement learning, which is training affordable, theoretically interpretable and self-adapting. To show the idea more concretely, a novel channel estimation algorithm is proposed in the multiple-input single-output (MISO) system in the case where the noise model is unknown. In particular, we utilize a universal mixture of Gaussian (MoG) model, which can adaptively adjust the involved parameters to fit the true noise distribution by using observed data. We propose a novel variational inference framework to achieve automatical noise model refinement and design the corresponding online channel estimator. To reduce the online algorithm overhead, we propose a decoupled variational Bayesian method to achieve linear computational complexity. Simulations show that our proposed method outperforms both the model-based and data-based counterparts.