Semi-Passive Intelligent Reflecting Surfaces Aided Channel Estimation for Vehicular Networks

Intelligent Reflecting Surfaces (IRS) have appeared as a cutting edge strategy to augment channel estimation and amplify network efficiency in high-mobility vehicular networks. The semi-passive IRS contains semi-passive and passive elements, and changing the phase of these elements can enhance control of signals precisely. In this paper, the semi-passive IRS aided channel estimation is investigated to mitigate the multipath fading and channel error, thereby improving the achievable rate and channel estimation accuracy. This novel methodology is presented by the implementation of phase optimization of the IRSdynamic channel estimation technique and IRS deployment strategy. In IRS aided channel estimation, the Spatial Base Extended Model (SBEM) algorithm is used to simplify the uplink channel estimation process and the Particle Swarm Optimisation (PSO) the IRS phase to improve the channel estimation accuracy. Simulation result shows the method can to significantly improve the communication efficiency and channel estimation accuracy within the high-mobility vehicular networks.