Semantic Learning for Molecular Communication in Internet of Bio-Nano Things

TL;DR

This paper introduces a semantic learning framework for molecular communication in IoBNT, enhancing diagnostic accuracy by focusing on task-relevant information and modeling molecular channels with deep learning techniques.

Contribution

It presents a novel end-to-end deep learning approach that optimizes task-specific molecular communication, incorporating a probabilistic channel model for improved performance.

Findings

Diagnostic accuracy improved by at least 25%

Effective extraction and decoding of semantic features

Enhanced performance under resource constraints

Abstract

Molecular communication (MC) provides a foundational framework for information transmission in the Internet of Bio-Nano Things (IoBNT), where efficiency and reliability are crucial. However, the inherent limitations of molecular channels, such as low transmission rates, noise, and intersymbol interference (ISI), limit their ability to support complex data transmission. This paper proposes an end-to-end semantic learning framework designed to optimize task-oriented molecular communication, with a focus on biomedical diagnostic tasks under resource-constrained conditions. The proposed framework employs a deep encoder-decoder architecture to efficiently extract, quantize, and decode semantic features, prioritizing taskrelevant semantic information to enhance diagnostic classification performance. Additionally, a probabilistic channel network is introduced to approximate molecular propagation dynamics, enabling gradient-based optimization for end-to-end learning. Experimental results demonstrate that the proposed semantic framework improves diagnostic accuracy by at least 25% compared to conventional JPEG compression with LDPC coding methods under resource-constrained communication scenarios.