Non-Data-Aided SNR Estimation for Molecular Communication Systems in Internet of Bio-Nano Things

This article proposes a nondata-aided (NDA) signal-to-noise ratio (SNR) estimation method for molecular diffusive communication channels. In contrast to previous data-aided (DA) approaches, which require a known data sequence to be shared between transmitter and receiver nanomachines, the proposed NDA method eliminates the need for prior knowledge of data symbols for the estimation process. The absence of a training sequence significantly reduces resource costs by minimizing the number of molecules required for information transmission. The expectation–maximization (EM) algorithm was used to iteratively find the maximum-likelihood (ML) estimates. The proposed method is particularly advantageous in scenarios in which accessing known data sequences is not possible, thereby ensuring robust communication in the Internet of Bio-Nano Things. By employing the ML estimates obtained through the EM algorithm, the probabilities associated with the transmission of data symbols were derived, and the bit error probabilities were obtained to assess the system performance. The numerical results validated the effectiveness of the proposed NDA SNR estimator in accurately estimating SNR over a diverse range of conditions. The proposed NDA estimator demonstrated resilience by maintaining a low bit error probability.