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

• URL: http://arxiv.org/abs/2502.08426v1
• Date: Wed, 12 Feb 2025 14:09:05 GMT
• Title: Semantic Learning for Molecular Communication in Internet of Bio-Nano Things
• Authors: Hanlin Cai, Ozgur B. Akan,
• Abstract summary: This paper proposes an end-to-end semantic learning framework to optimize task-oriented molecular communication. The proposed framework employs a deep encoder-decoder architecture to efficiently extract, quantize, and decode semantic features. Experimental results demonstrate that the proposed semantic framework improves diagnostic accuracy by at least 25% compared to conventional JPEG compression.
• Score: 0.0
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• 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 inter-symbol 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 task-relevant 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.

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