Maximum Likelihood Estimation of SNR for Diffusion-Based Molecular Communication

Abstract

We investigate the problem of estimation of a signal-to-noise ratio (SNR) measure for diffusion-based molecular communication (DMC) systems. Considering molecular concentration-encoded signal with residual noise, we present the SNR estimation using maximum likelihood principle with sampled observations. Moreover, we derive the Cramer–Rao lower bound and assess the performance of our proposed estimator in terms of its mean square error. Our analysis helps in designing a reliable DMC system with a memoryless receiver nanomachine.