Analysis and Optimization for IRS-Aided Covert Communications with Finite-Alphabet Inputs
Manlin Wang, Xing Lv, Zhen Ming Xu, Bin Xia · 2025
The existing works on intelligent reflecting surface (IRS) aided covert communications consider the Gaussian input, which is however infeasible in practical systems. Two core issues remain to be answered: 1) How much performance gain can be obtained by applying the IRS for covert communications with finite-alphabet inputs? 2) How to jointly design the highly coupled parameters (constellation distribution and reflection coefficients) to obtain the optimal performance? To address these issues, in this work, the performance of the IRS aided covert communications with finite-alphabet inputs is analyzed, and a joint optimization scheme is proposed. In particular, the channel cutoff rate (CR) and the lower bound of the average detection error probability at the warder are derived under fading channels. Further, the impact of the reflection coefficients on the performance is discussed to reveal the benefits brought by the IRS. In addition, a two-layer algorithm is proposed to maximize the channel CR, where a channel variance tuple is introduced to decouple the optimization variables (constellation distribution and reflection coefficients). Numerical simulation demonstrates the superiority of the proposed scheme over various benchmarks. Moreover, the stricter the covertness constraint, the more concentrated the optimal probability distribution is at central constellation points.