IRS-Assisted Joint Broadcast/Multicast Resource Allocation and Cooperative Perception Optimization for 6G V2X Networks

Aiming at the coexistence challenges of highly reliable broadcast/multicast communication and high-precision cooperative sensing in 6G vehicular networking, this paper proposes an IRS-enhanced joint optimization framework. First, an IRS-assisted millimeter-wave-terahertz heterogeneous network architecture is constructed to reconfigure multi-vehicle communication links through dynamically programmable reflective units to solve the signal occlusion and interference problems in traditional multicast scenarios; second, a cooperative sensing task modeling method based on GAT is designed to fuse LiDAR point-cloud data and camera information into spatio-temporal sensing features to drive dynamic resource allocation demand; finally, the HFRL algorithm is proposed to jointly optimize the IRS phase matrix, broadcast spectrum allocation, computational offloading strategy and perception task priority. The simulation results show that the sensing accuracy has increased from 68.5% of the baseline to 90.5%, the URLLC reliability is 99.999%, and the spectral efficiency is 5.2 bps/Hz.