title: A nonparametric stochastic optimizer for TDMA-based neuronal signaling.
abstract: This paper considers neurons as a physical communication medium for intrabody networks of nano/micro-scale machines and formulates a noisy multiobjective optimization problem for a Time Division Multiple Access (TDMA) communication protocol atop the physical layer. The problem is to find the Pareto-optimal TDMA configurations that maximize communication performance (e.g., latency) by multiplexing a given neuronal network to parallelize signal transmissions while maximizing communication robustness (i.e., unlikeliness of signal interference) against noise in neuronal signaling. Using a nonparametric significance test, the proposed stochastic optimizer is designed to statistically determine the superior-inferior relationship between given two solution candidates and seek the optimal trade-offs among communication performance and robustness objectives. Simulation results show that the proposed optimizer efficiently obtains quality TDMA configurations in noisy environments and outperforms existing noise-aware stochastic optimizers.
authors.forename: Junichi
authors.initials: J
authors.lastname: Suzuki
authors.forename: Dũng H
authors.initials: DH
authors.lastname: Phan
authors.forename: Harry
authors.initials: H
authors.lastname: Budiman
mesh_terms: D000465:Algorithms; D002450:Cell Communication / Q000502:physiology*; D003198:Computer Simulation; D039301:Computers, Molecular; D006801:Humans; D008959:Models, Neurological; D036103:Nanotechnology / Q000379:methods*; D009474:Neurons / Q000502:physiology*; D013269:Stochastic Processes