Roadside Intelligence: Efficient Channel Estimation for IRS-Aided mmWave Vehicular Communication

• Published in: IEEE access 2024, Vol.12, p.115883-115894
• Main Authors: Nandan, S., Abdul Rahiman, M.
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Angle of arrival; Channel estimation; Communication; Communications systems; Complexity; compressive sensing; Energy consumption; Extended Kalman filter; high mobility; intelligent reflecting surface; Matched pursuit; Millimeter wave communication; millimeter wave communications; Millimeter waves; Predictions; Real time; Reconfigurable intelligent surfaces; Roadsides; Sensors; Vehicle dynamics; Vehicular ad hoc networks; vehicular communication; Wireless communication
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3445528

Fifth-generation(5G) and beyond communication systems, assisted by Intelligent Reflecting Surfaces (IRS), often encounter hindrances such as unreliable connections, high energy usage, and prolonged latency. Channel estimation in IRS-aided systems is challenging in vehicular communication systems with roadside IRS units and fast-moving users. This paper proposes an efficient and low-complex channel estimation strategy for high-speed vehicular mmWave communication systems equipped with roadside IRS. The method consists of two stages, sensing and prediction, which aim to improve efficiency and accuracy under dynamic channel conditions. In the sensing phase, an initial assessment of channel characteristics is estimated by exploiting the sparse nature of the channel. We use the Compressive Sampling Matching Pursuit (CoSaMP) algorithm for accurate estimation with reduced computational complexity. The prediction stage consists of real-time tracking and prediction of the Angle of Arrival (AoA) and the Angle of Departure (AoD) using the Extended Kalman Filter (EKF). This ensures more accurate dynamic channel estimation based on predicted array response vectors without increasing the pilot overhead. Simulation results demonstrate that our proposed approach can offer precise channel estimation with significantly reduced training overhead.