Intelligent Roadside Reflection: Efficient Passive Beamforming for IRS Aided mmWave Vehicular Communication

Abstract

Fifth-generation (5G) and beyond communication systems, supported by Intelligent Reflecting Surfaces (IRS), frequently face challenges like less reliable communication, increased energy consumption, and high latency. Passive beamforming at the reflecting surfaces is essential to enhance the received signal quality, coverage, and overall performance of the system. Implementing passive beamforming in IRS-aided systems poses challenges in vehicular communication environments, particularly with roadside IRS units and fast-moving users. This paper proposes an efficient and moderately low-complexity passive beamforming algorithm for high-velocity vehicular communication systems using roadside IRS in mmWave networks. The algorithm optimizes the IRS reflection coefficients to enhance beamforming using the proposed Successive Convex Approximation based Interior Point Method (SCA-IPM). The algorithm iteratively linearizes the objective function and constraints, incorporates a barrier function for stability, and uses Newton's method for updates. This method efficiently handles non-convex optimization problems and improves signal quality in dynamic IRS-based vehicular communication systems. The simulation results show that the proposed method delivers a higher data rate and improves the received signal-to-noise ratio (SNR), with moderately low system complexity.