The Crossover-Distance for ISI-Correcting Decoding of Convolutional Codes in Diffusion-Based Molecular Communications

TL;DR

This paper introduces a crossover distance metric for diffusion-based molecular communication, improving decoding reliability of convolutional codes by approximating maximum likelihood estimation amidst ISI effects.

Contribution

A novel crossover distance metric and a decoding scheme based on it are proposed, enhancing system performance in molecular communication channels affected by ISI.

Findings

Significant performance improvement over uncoded systems.

Proposed decoding approximates maximum likelihood estimation.

Convolutional codes outperform existing codes at same throughput.

Abstract

In diffusion based molecular communication, the intersymbol interference (ISI) is an important reason for system performance degradation, which is caused by the random movement, out-of-order arrival and indistinguishability of the moleclues. In this paper, a new metric called crossover distance is introduced to measure the distance between the received bit sequence and the probably tranmitted bit sequences. A new decoding scheme of conventional codes is proposed based on crossover distance, which can enhance the communication reliability significantly. The theoretic analysis indicates that the proposed decoding algorithm provides an approximately maximal likelihood estimation of the information bits. The numerical results show that compared with uncoded systems and some existing channel codes, the proposed convolutional codes offer good performance with same throughputs.