Improving Reliability Performance of Diffusion-based Molecular Communication With Adaptive Threshold Variation Algorithm

TL;DR

This paper proposes an adaptive threshold variation algorithm to enhance the reliability of diffusion-based molecular communication by reducing bit error rates through a novel classification and analysis model.

Contribution

It introduces a molecular classified model and an adaptive threshold variation algorithm to improve communication reliability in molecular systems.

Findings

Significant BER reduction demonstrated in simulations.

Adaptive algorithm outperforms fixed threshold methods.

Improved SINR expression validates system performance.

Abstract

In this work, we investigate the communication reliability for diffusion-based molecular communication, using the indicator of bit error rate (BER). A molecular classified model is established to divide molecules into three parts, which are the signal, inter-symbol interference (ISI) and noise. We expand each part separately using molecular absorbing probability, and connect them by a traditional-like formula. Based on the classified model, we do a theoretical analysis to prove the feasibility of improving the BER performance. Accordingly, an adaptive threshold variation (ATV) algorithm is designed in demodulation to implement the goal, which makes the receiver adapt the channel condition properly through learning process. Moreover, the complexity of ATV is calculated and its performance in various noisy channel is discussed. An expression of Signal to Interference plus Noise Ratio (SINR) is defined to verify the system performance. We test some important parameters of the channel model, as well as the ATV algorithm in the simulation section. The results have shown the performance gain of the proposal.