Error probability analysis under H-noise scenarios in diffusive molecular communication

Diffusive Molecular Communication (DMC) represents a critical paradigm in nanoscale communication, yet various noise models significantly influence its performance. This paper presents an analytical framework for evaluating error probability under H-noise. This novel noise model accounts for anomalous diffusion scenarios, including sub-diffusion, super-diffusion, and normal diffusion. Unlike conventional noise models that primarily focus on normal diffusion, H-noise provides a unified characterization of uncertainty in molecular propagation across diverse diffusion environments. The study introduces a mathematical formulation of error probability, integrating parameters such as decision thresholds, binary transmission probability, and diffusion coefficients. Numerical simulations validate the theoretical analysis, demonstrating the impact of scenario parameters on error probability and the statistical behavior of molecular arrival times. In addition to error analysis, this study explores broader applications of DMC in biomedical systems, environmental monitoring, and nanoscale computing, highlighting its potential beyond intelligent transportation systems. This work enhances the understanding of DMC under complex noise conditions by delineating different evaluation metrics and extending the discussion to a broader spectrum of applications. It provides insights into optimizing molecular communication systems for future nano-networking applications.