Secrecy Capacity Maximization for IRS-Assisted High-Speed Train Communications

In this letter, we propose an IRS-assisted MIMO communication system model in the presence of an eavesdropper (Eve). A distance-dependent Rician factor and real-time Doppler frequency offset (DFO) compensation mechanismare utilized to characterize highly time-varying wireless channels. The secrecy capacity optimization problem is formulated by jointly optimizing the transmit beamforming, artificial noise (AN) matrix, and IRS phase shifts, subject to constraints on the maximum transmit power and unit-modulus IRS phase shifts. To solve the non-convex problem, the weighted minimum mean square error (WMMSE) algorithm transforms it into a convex optimization problem, and the optimal solution is derived using an alternating optimization strategy with coupled variables. Simulation results show that the optimization algorithm proposed in this letter converges quickly and achieves higher secrecy capacity.