Executive Summary : | The research on reconfigurable holographic surface (RHS)-based wireless communication is in its early stages, but it has the potential to enhance network capacity by exploiting spatial diversity through large-scale phased arrays. However, accurate beamforming in mMIMO requires many phase shifters and power amplifiers, leading to high hardware costs and power consumption. The RHS can serve as a transmit/receive antenna integrated with a transceiver to generate beams in desired directions. The research on RHS-based wireless communication is in its early stages, and traditional models for transceivers may not be suitable for determining its fundamental limits. Developing a hybrid beamforming scheme for multi-user RHS-based wireless systems is challenging, and existing algorithms have focused primarily on enhancing the sum rate of multi-user RHS-based wireless networks.
This project aims to address these challenges by developing physical layer algorithms and protocols for RHS-based wireless communication. The team will apply electromagnetic information theory (EIT) to develop a channel model for RHS-based transmission, and develop novel low-complexity hybrid beamforming algorithms to maximize sum-rate and energy-efficient communications. Machine learning methods will be utilized to identify the best beamforming weights through training data analysis, leading to enhanced system performance in terms of metrics like sum-rate and energy efficiency. The research will also explore a new networking scenario that uses RHS as the transceiver and RIS as the reflector to steer signals in high blockage scenarios. Finally, the team will fabricate an RHS and develop an experimental setup to validate the developed algorithms in the radio-frequency (RF) band. |