Channel Estimation and Phase Optimization for RIS-Assisted LoRa in IIoT Networks

Abstract

Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology to enhance spectral efficiency and reduce energy consumption in next-generation wireless networks. Their ability to dynamically reconfigure the wireless propagation environment is particularly advantageous for sub-GHz systems such as Long Range (LoRa), which is widely adopted in Industrial Internet of Things (IIoT) applications but often suffers from unreliable links due to harsh and dynamic propagation conditions.The primary objective of this work is to propose a RIS-assisted LoRa system aimed at enhancing performance compared to the conventional LoRa system. The second objective is to optimize the performance of the proposed system through the selection of RIS phase shifts. Since this optimization relies on accurate knowledge of the channel characteristics, the process is divided into two main steps: (i) a channel estimation phase based on periodic training, and (ii) the determination of the optimal RIS phase shifts using a gradient-based algorithm.The proposed framework is evaluated against baseline schemes, including random phase (RP) selection and phase matching (PM). Simulation results demonstrate significant improvements in bit error rate (BER) and energy efficiency. These results highlight the potential of gradient-based RIS control to enhance the reliability and scalability of RIS-enabled Low Power Wide Area Network (LPWAN) deployments in industrial environments.