Ghalib H. Alshammri*, Walid K. M. Ahmed and Victor B. Lawrence Pages 1 - 10 ( 10 )
Background: The architecture and sequential learning rule-based underlying ARFIS (adaptive-receiver-based fuzzy inference system) is proposed to estimate and predict the adaptive threshold-based detection scheme for diffusion-based molecular communication (DMC).
Method: 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 IF-THEN rules. The ARFIS architecture is employed to model nonlinear molecular communication to predict the received bits over time series.
Result: 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 reception noise.
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 a promising improvement in the un-coded bit error rate (BER) compared with other threshold-based detection schemes in the literature.
Molecular communication, diffusion-based, ISI, reception noise, OOK modulation technique, artificial neural network, adaptive threshold scheme, fuzzy inference system
Electrical and Computer Engineering Department, Stevens Institute of Technology, Hoboken, New Jersey, Electrical and Computer Engineering Department, Stevens Institute of Technology, Hoboken, New Jersey, Electrical and Computer Engineering Department, Stevens Institute of Technology, Hoboken, New Jersey