In this paper, we develop a mathematical framework for modeling time-variant stochastic channels in diffusive mobile molecular communication (MC) systems. In particular, we consider a diffusive mobile MC system consisting of a pair of transmitter and receiver nano-machines suspended in a fluid medium with a uniform bulk flow, where we assume that either the transmitter, or the receiver, or both are mobile and we model the mobility of the nano-machines by Brownian motion. The transmitter and receiver nano-machines exchange information via diffusive signaling molecules. Due to the random movements of the transmitter and receiver nano-machines, the statistics of the channel impulse response (CIR) change over time. In this paper, we introduce a statistical framework for characterization of the impulse response of time-variant MC channels. To this end, we derive closed-form expressions for the mean, the autocorrelation function, the cumulative distribution function (CDF), and the probability density function (PDF) of the time-variant CIR. Given the autocorrelation function, we define the coherence time of the time-variant MC channel as a metric for characterization of the variations of the impulse response. The derived CDF is employed for calculation of the outage probability of the system. We also show that under certain conditions, the PDF of the CIR can be accurately approximated by a Log-normal distribution. Given this approximation, we derive a simple model for outdated channel state information (CSI). Moreover, we derive an analytical expression for evaluation of the expected error probability of a simple detector for the considered MC system. In order to investigate the impact of CIR decorrelation over time, we compare the performances of a detector with perfect CSI knowledge and a detector with outdated CSI knowledge. The accuracy of the proposed analytical expressions is verified via particle-based simulation of the Brownian motion.
Authors of REF studied the statistical characterization of time-varying CIR for a single-link MC channel.
Stochastic Channel Modeling for Diffusive Mobile Molecular Communication Systems
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Computer Science Mathematics