Molecular Communication (MC) has emerged as a cutting edge technique for exchanging and conveying information among nano-devices in very small dimensions or specific environments, such as water, tunnels, and human bodies. It is worthwhile noting that existing simulation algorithms for diffusion-based MC systems incur high computational complexity when simulating the absorption of molecules at receiver(s). Specifically, the existing algorithms require a very small simulation time step length to accurately model the absorption, leading to a long simulation run time. Against this background, this thesis aims to reduce the computational complexity for the simulation of absorption at receiver(s) in a diffusion-based MC system.
In Chapter 3, the system models of the investigated problem are introduced. To be specific, the MC system with both a single absorbing receiver and that with multiple absorbing receivers are considered. The analytical reaction probabilities of molecules with absorbing receiver(s) are discussed. Furthermore, the intra-step absorption probabilities used in algorithms for simulating absorbing receiver(s) are presented.
In Chapter 4, existing simulation algorithms for absorbing receiver(s) in diffusion-based MC systems are carefully examined and the similarities and differences among algorithms are discussed. To quickly predict the simulation accuracy of an existing algorithm, the refined Monte Carlo (RMC) algorithm, a new expression is proposed as a function of the simulation time step length and system parameters. After discovering that the RMC algorithm enables accurate simulation for a relatively small simulation time step length only, a novel a priori Monte Carlo (APMC) algorithm is proposed to accurately simulate the molecules absorbed at spherical absorbing receiver(s) with low computational complexity for relatively large simulation time step lengths. Moreover, by analyzing the computational complexity of the APMC algorithm and the RMC algorithm, a likelihood threshold is proposed to reduce the computational complexity for both algorithms.
In Chapter 5, numerical results are shown to evaluate the aforementioned simulation algorithms. It is obvious from the results that using the prediction expression for the RMC algorithm, we can characterize the accuracy of the simulation results of the RMC algorithm without running it, which facilitates the selection of simulation time step length for a given system. It is also demonstrated that the APMC algorithm effectively overcomes the shortcoming of the existing algorithms. It is further shown that after applying an appropriate likelihood threshold to the APMC algorithm and the RMC algorithm, the computational complexity is significantly saved while only an extremely small loss in accuracy is caused
