Modeling and Analysis of Cooperative and Large-scale Molecular Communication Systems
Creators: name: Fang, Yuting
Publisher: name: The Australian National University
Year: 2020

description: Molecular communications (MC) is the use of molecules as carriers of information between devices. In MC, there are several main research challenges: 1) Low reliability of diffusionbased MC systems, 2) optimal MC system design, 3) understanding cooperation among the microscopic population with noisy signaling, and 4) realistic molecular information propagation environments. To deal with these challenges in MC, this thesis focuses on the four main issues: 1) How to improve reliability of diffusion-based MC systems, 2) how to design practical suboptimal cooperative MC systems, 3) what the impact of noisy molecular signaling on the bacterial cooperation behavior is, and 4) how the communication performance changes when molecules transport in a realistic environment. First, we study cooperative detection among multiple distributed receivers (RXs) in a diffusion-based MC system. Unlike most existing studies that consider one-phase noisy transmission or one-symbol transmission for simplicity, we consider multiple-symbol transmission and two-phase noisy transmission from a transmitter (TX) to a fusion center (FC) via multiple RXs. The FC uses hard fusion rules to arrive at a final decision. We derive the system error probability. We formulate the suboptimal convex optimization problems to determine the optimal decision thresholds. We show that the system error performance is greatly improved by combining the detection information of distributed RXs. Second, we propose symbol-by-symbol maximum likelihood (ML) detection for a cooperative diffusion-based MC system. Different from the first work, the FC uses the likelihood of its observations from all RXs to make a decision on the transmitted symbol in each interval. We propose three ML detection variants according to different RX behaviors and different knowledge at the FC. We derive the system error probabilities for two ML detector variants. We also optimize the molecule allocation among RXs for one variant. We show that simpler and non-ML cooperative variants studied in the first work have error performance comparable to ML detectors. Third, we present an analytically tractable model for predicting the statistics of the number of cooperative microorganisms. Unlike prior studies that considered abstract signal propagation channels among microorganisms, we use diffusion-reaction equations to accurately characterize signal received at each microorganism due to independent diffusion and degradation of molecules. Microorganisms are randomly distributed in a two-dimensional (2D) environment where each one continuously releases molecules at random times. We derive the 2D channel response due to one bacterium or randomly-distributed bacteria. We then derive the expected probability of cooperation at the bacterium. We finally derive the moment generating function and different statistics for the number of cooperators. Our model can be used to predict the impact of noisy signaling, e.g., diffusion coefficient and reaction rate, on the statistics of the number of responsive cooperators in QS. Last, we investigate the communication through realistic porous channels for the first time via statistical breakthrough curves. Assuming that the number of arrived molecules can be approximated as a Gaussian random variable and using fully resolved computational fluid dynamics results for the breakthrough curves, we present the numerical results for the throughput, mutual information, error probability, and information diversity gain. We reveal the unique characteristics of the porous medium channel. This thesis serves an unprecedented way to enable 1) high-accuracy disease detection and health monitoring and 2) bacterial infection prevention and new environmental remediation. It also provides useful insights for designing the optimal MC systems through porous media and the optimal cooperative MC systems.