Capacity Bounds and Low-Complexity Constellation Shaping under Mixed Gaussian-Impulsive Noise

This letter investigates channel capacity bounds and constellation shaping for memoryless mixed Gaussian-impulsive noise. We derive capacity bounds utilizing the entropy power inequality and the dual capacity expression. We demonstrate that these bounds become asymptotically tight, yielding a closed-form asymptotic expression for the channel capacity. Guided by this theoretical analysis, we propose a low-complexity, non-iterative constellation shaping method. Simulation results validate the tightness of the derived bounds and confirm that the proposed shaped constellation achieves the highest mutual information among the considered baselines.