IRS-Assisted High-Speed Train Communications: Performance Analysis and Optimal Configuration

High-speed train (HST) communications are envisioned to provide diversified broadband services by integrating with 5G while the high mobility induces fast-fading channels and potentially degrades the system performance. To address this issue, we investigate an HST communication network empowered by intelligent reflecting surfaces (IRSs) with the multiple-input–multiple-output (MIMO) technology. Statistical channel state information (CSI) is exploited to mitigate the impact of the fast time-varying fading. The transceiver beamforming vectors and the IRS phase shift matrix are optimized to improve the system performance in terms of the outage probability and the ergodic capacity considering the channel uncertainty. First, we derive the analytical expression of the outage probability with a generalized Marcum
$Q $
-function. Then, we develop an alternating optimization algorithm to minimize the outage probability by capitalizing on the generalized eigenvalue–eigenvectors. Moreover, the ergodic capacity is deduced with statistical CSI and then optimized by analyzing the upper bound with Jensen’s approximation. Extensive simulations show that simulation results are consistent with the theoretical analysis, and the IRS-assisted system significantly outperforms the system without IRS in terms of the outage probability and the ergodic capacity.