Secrecy Enhancement for Media-Based Modulation via Probabilistic Optimization

Media-based modulation (MBM) is a novel modulation scheme, which embeds information in different channel fading realizations created by changing the status of radio frequency mirrors. In this letter, we propose a new secrecy enhancement scheme based on probabilistic shaping for MBM systems. The secrecy rate with an arbitrary probability mass function (PMF) of the input MBM symbols is analyzed and an iterative algorithm utilizing the gradient method with backtracking line search is proposed to find the optimal PMF. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.