Channel Estimation via Model and Learning for Monostatic Multiantenna Backscatter Communication

Abstract

Backscatter communication has received considerable attention for future Internet-of-things (IoT) exploiting battery-free devices. To support massive connectivity of such energy-constrained IoT devices, a monostatic multiantenna backscatter communication network (MBCN) with beamforming has emerged, for which reliable channel estimation is indispensable. This paper tackles backscatter and forward channel estimation problems for a monostatic MBCN in a generalized fading model, where the optimal minimum mean square error (MMSE) solutions are too intricate to derive due to the complicated distribution function of the cascaded backscatter channel. After deriving the linear MMSE (LMMSE) estimator of the backscatter channel in the generalized fading model, we propose learning-based estimators based on the fast and flexible convolutional neural network (FFDNet) toward the optimal solution. We also propose a deep neural network (DNN) with a customized loss function that estimates the forward channel coefficients directly from the backscattered signal. The results show that the proposed FFDNet-based estimator for the backscatter channel reduces the MSE of the LMMSE estimator by a factor of two or three. In addition, the DNN-based estimator for the forward channel is shown to reduce the pilot overhead up to by half when compared with the conventional estimator.

Keywords

• Backscatter communication
• channel estimation
• deep learning
• linear minimum mean square error
• multiple antennas