Joint Optimization for Hybrid Active-Passive IRS-Assisted OTFS Systems Under Large-Scale Fading

Abstract

Orthogonal time frequency space (OTFS) and hybrid active-passive intelligent reflecting surface (HIRS) are promising technologies for next-generation wireless communications. However, related research on HIRS-assisted OTFS systems under large-scale fading is still scarce, lacking efficient optimization schemes to tackle cascaded attenuation and amplified active noise. To address these issues, we establish a HIRS-assisted OTFS system model under large-scale fading. For this system, a jointly enhanced dynamic majorization-minimization based alternating optimization (JE-DMM-AO) algorithm is developed to maximize the signal-to-interference-plus-noise ratio (SINR). Specifically, the algorithm leverages an end-to-end cascaded energy scheduling scheme to secure a superior initial state, followed by an adaptive Nesterov-like extrapolation mechanism that significantly accelerates convergence during the optimization of the reflecting elements. Simulation results demonstrate that the proposed algorithm significantly improves the signal detection reliability of the HIRS-OTFS system with a lower computational complexity.