Joint channel training and passive beamforming design for intelligent reflecting surface-aided LoRa systems

In order to examine the potential and synergetic aspects of intelligent reflecting surface (IRS) techniques for Internet-of-Things (IoT), we study an IRS-aided Long Range (LoRa) system in this paper. Specifically, to facilitate the acquisition of accurate channel state information (CSI) for effective reflection of LoRa signals, we first propose an optimal training design for the least squares channel estimation with LoRa modulation, and then, by utilizing the acquired CSI, we develop a high-performing passive beamforming scheme based on a signal-to-ratio (SNR) criterion. Numerical results show that the proposed training design considerably outperforms the baseline schemes, and the proposed passive beamforming design results in a significant improvement in performance over that of the conventional LoRa system, thereby demonstrating the feasibility of extending coverage areas of LoRa systems with the aid of IRS.