Background: The architecture and sequential learning rule-based underlying ARFIS
(adaptive-receiver-based fuzzy inference system) are proposed to estimate and predict the adaptive
threshold-based detection scheme for diffusion-based molecular communication (DMC).
Methods: The proposed system forwards an estimate of the received bits based on the current molecular
cumulative concentration, which is derived using sequential training-based principle with weight
and bias and an input-output mapping based on both human knowledge in the form of fuzzy IFTHEN
rules. The ARFIS architecture is employed to model nonlinear molecular communication to
predict the received bits over time series.
Results: This procedure is suitable for binary On-OFF-Keying (Book signaling), where the receiver
bio-nanomachine (Rx Bio-NM) adapts the 1/0-bit detection threshold based on all previous received
molecular cumulative concentrations to alleviate the inter-symbol interference (ISI) problem and
Conclusion: Theoretical and simulation results show the improvement in diffusion-based molecular
throughput and the optimal number of molecules in transmission. Furthermore, the performance
evaluation in various noisy channel sources shows promising improvement in the un-coded bit error
rate (BER) compared with other threshold-based detection schemes in the literature.