Channel Modeling and Optimal Released Molecules for Mobile Molecular MIMO Communications Among Bionanosensors

In this article, we study a mobile molecular multiple-input multiple-output (MIMO) communication among bionanosensors, in which the transmitters and receivers are both mobile bionanosensors. The channel impulse response (CIR) of this mobile molecular MIMO system changes over time due to the stochastic movements of both transmitters and receivers. First, we use simulation results obtained by Brownian simulators to mathematically model the time-variable CIR. Then, based on the modeled CIR, we derive the numerical expression of the average bit error rate (BER) of this system. Besides, a multivariable optimization problem has been formulated to minimize the average BER of the mobile molecular MIMO system by optimizing the number of molecules released by each transmitter, which is constrained by lower and upper bounds. Finally, a novel algorithm based on deep neural network (DNN) is presented to achieve the optimal solution of the multivariable optimization problem. The numerical results reveal that the DNN-based optimization algorithm has a shorter running time and lower average BER in comparison with the classic genetic algorithm (GA). The optimization results by this algorithm are approximate to the optimal solutions found by the exhaustive search.