Robust Deep Learning for Uplink Channel Estimation in Cellular Network Under Inter-Cell Interference
Huayan Guo, Vincent K. N. Lau
Abstract
Deep learning (DL)-based channel estimation has achieved remarkable success. However, most existing works focus on the white Gaussian noise which are inapplicable for cell-edge users under inter-cell interference (ICI). In this paper, we address this issue by proposing a novel DL-based channel estimation solution with a cascaded model-based and model-free deep neural network (DNN) structure. Specifically, the model-based module is designed by the variational Bayesian inference (VBI) technique to suppress the time-varying ICI, and the model-free module is designed by the Denoising Sparse Autoencoder (DSAE) structure to further refine the channel estimation. The proposed DNN is firstly pre-trained by offline supervised training, and various channel statistics are encapsulated in the DNN weights with the assist of a hyper-prior net modelling different sparse priors for different training samples. Then, an online Bayesian learning algorithm is proposed to train the model-based VBI module based on real-time pilot samples to track the online channel statistics. Simulation results verify that the proposed solution outperforms various state-of-the-art baseline schemes in a large SINR range with comparable performance to the estimator with genie-aided channel statistics.