Channel Modeling for Multi-Receiver Molecular Communication Systems

TL;DR

This paper develops and analytically derives a channel model for molecular SIMO systems with multiple fully absorbing receivers, including a simplified version, and verifies their accuracy through simulations.

Contribution

It introduces the first analytical channel model for molecular SIMO systems with multiple receivers, including a recursive closed-form solution and a less complex approximation.

Findings

The model accurately estimates channel response compared to simulations.

The simplified model offers a good trade-off between accuracy and computational complexity.

Performance varies with system topology and parameters.

Abstract

Molecular Communication via Diffusion (MCvD) is a prominent small-scale technology, which roots from the nature. With solid analytical foundations on channel response and advanced modulation techniques, molecular single-input-single-output (SISO) systems are one of the most studied molecular networks in the literature. However, the literature is yet to provide sufficient analytical channel modeling on molecular multiple-output systems with fully absorbing receivers, {one of the common applications in the area. In this paper, a channel model for molecular single-input-multiple-output (SIMO) systems is proposed for estimating the channel response of such systems. With the model's recursive nature, the closed-form solution of the channel response of molecular 2-Rx SIMO systems is analytically derived. A simplified model with lower complexity is also presented at a cost of slightly less accurate channel estimation. The models are extended to the molecular SIMO systems with more than two receivers. The performance of the methods are evaluated for several topologies with different parameters, and the accuracy of the model is verified by comparing to computer-simulated channel estimations in terms of quantitative error metrics such as root-mean-squared error. The performance of the simplified model is verified by the amount of deviation, indicating sufficient channel modeling performance with reduced computational power.