ANIS: Anti-Noise ISI-Suppression Filter for Molecular Communication via Diffusion

Molecular Communication via Diffusion (MCvD) is a viable communication paradigm for nanonetworks, particularly in fluidic biological environments where bio-nanonetworks operate. Two significant factors that degrade the MCvD signal are noise and Inter-Symbol Interference (ISI). The expected Channel Impulse Response (CIR) of MCvD exhibits a long, slowly attenuating tail due to molecular diffusion, leading to ISI and consequently limiting the data rate. The suppression of ISI and noise is crucial for enhancing the effectiveness of MCvD systems, especially at higher data rates. Although various ISI-suppression methods have been explored, they are often treated as secondary components in broader topics, such as signal detection or modulation. Moreover, most current ISI-suppression techniques subtract the estimated ISI from the total signal. In this study, we introduce a novel approach to ISI-suppression through the use of filters, which eliminate ISI without requiring an ISI estimation. We explore the principles underlying ISI-suppression filters in MCvD and propose the Anti-Noise ISI-Suppression (ANIS) filter with robust anti-noise capabilities, accompanied by a signal detection scheme tailored for MCvD scenarios afflicted by both ISI and noise. We compare our proposed ANIS filter against state-of-the-art detection approaches. The results demonstrate that our ANIS filter can effectively recover signals severely degraded by both ISI and noise.