Noise Characterization and Robust Signal Detection in Yeast Pheromone Molecular Communication

A critical aspect of Molecular Communications (MC) is the implementation of signal detection policies amidst noise. To date, noise characterizations within the MC field have predominantly drawn from methodologies found in wireless communications literature. In this study, we diverge from existing MC research by utilizing a newly developed experimental platform that employs yeast, allowing us to consider more realistic noise characterizations based on the relevant signaling pathways. We propose suitable signal detection mechanisms tailored to this experimental setup, which focuses on yeast cell-to-cell communications. Our analysis identifies gene transcription as the primary source of noise, and we utilize a Markov birth-death process model with Poisson arrivals and departures to characterize it. The noisy expression of the FUS1 gene is best represented using a mixed Gaussian distribution model. This model serves as a foundation for evaluating the performance of Maximum Likelihood Detection mechanisms in terms of Bit Error Rate (BER) for both symbol-by-symbol and sequence transmission schemes. Error analysis indicates that appropriate adjustments to the signal threshold can reduce errors to as low as 10%, which is not negligible. In contrast, the detection of symbol sequences demonstrates enhanced error performance, achieving error rates as low as 0.4%, albeit at the cost of increased computational complexity.