Model-Driven Iterative Super-Resolution Channel Estimation for Wideband Near-Field Extremely Large-Scale MIMO Systems

Abstract

Channel estimation becomes challenging in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, since the channel sparsity in the angular domain is destroyed. To avoid designing a near-field sparse dictionary and the estimation errors caused by sparse transformation, in this letter, we formulate the wideband near-field channel estimation problem as an image super-resolution (SR) problem, and propose a model-driven iterative SR channel estimate network (MDISR-Net) based on the Bayesian principle. Specifically, each layer of MDISR-Net is composed of a convolutional neural network (CNN) followed by a gradient descent network, which are corresponding to the prior sub-problem and channel sub-problem. Stack of multiple layers enables channel estimation to be resolved iteratively by solving two sub-problems. Our proposed scheme does not require a sparse dictionary and hence avoid the estimation errors caused by sparse transformation. The numerical results demonstrate that MDISR-Net significantly improves channel estimation accuracy.