Hypernetwork-Aided Channel Estimation for Integrated Data and Energy Transfer

Integrated data and energy transfer (IDET) is an important component of future communication networks due to its characteristic to provide continuous and reliable wireless power to battery-limited devices. In order to get full advantages of IDET technology in future communication networks and realize efficient beamforming design, the base station (BS) must obtain accurate downlink channel state information (CSI). In this work, we propose an energy harvesting (EH) aided channel estimation scheme using a novel deep learning (DL) architecture in an end-to-end training mode. This architecture simultaneously obtains CSI from both an energy harvester and a data decoder at an IDET receiver. By designing a hypernetwork-aided deep neural network (DNN), we achieve more accurate channel estimation, while effectively reducing the pilot overhead in channel estimation. Simulation results show that compared to the state of the art, our EH aided channel estimation scheme attains a lower normalized mean square error (NMSE).