用语义学习提升生物纳米通信效率，专为资源受限的医疗诊断设计。
Semantic Learning for Molecular Communication in Internet of Bio-Nano Things
- 通过端到端语义编码解码架构，聚焦任务相关特征
- 在资源受限下诊断准确率提升至少25%
- 适合生物纳米网络中的医疗诊断场景
分子通信（MC）为生物纳米物联网（IoBNT）中的信息传输提供基础框架，效率与可靠性至关重要。然而，分子信道固有的局限性，如低传输速率、噪声和符号间干扰（ISI），限制了其对复杂数据传输的支持能力。本文提出一种面向任务的端到端语义学习框架，专用于资源受限条件下的生物医学诊断任务。该框架采用深度编码器-解码器结构，高效提取、量化并解码语义特征，优先保留任务相关语义信息以提升诊断分类性能。同时引入概率化信道网络，近似分子传播动态，支持基于梯度的端到端优化。实验表明，在资源受限通信场景下，该语义框架相比传统JPEG压缩结合LDPC编码方法，诊断准确率至少提升25%。
原文摘要 · Abstract (English)
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.
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