SNR Estimation with Rectangular Input Concentration in Molecular Communication Networks

The signal-to-noise ratio (SNR) is a critical statistic for evaluating channel parameter estimation, localization, and synchronization performance. Many situations necessitate SNR estimation information to assure the system’s dependability and efficacy. This paper proposes a method for SNR estimation for rectangular input concentration with inter-symbol interference and Brownian noise. In particular, we derive the closed-form expression with a maximum likelihood (ML) estimation approach for estimating SNR metrics. The receiver nanomachine uses the sampled observations of concentration encoded signal to estimate the SNR metric. Notably, the proposed method with rectangular concentration helps in designing more generic and realistic applications. The numerical results show the effectiveness of the proposed estimator.