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.

翻译：分子通信为生物纳米物联网中的信息传输提供了基础框架，其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、噪声及符号间干扰——制约了其支持复杂数据传输的能力。本文提出一种端到端语义学习框架，旨在优化面向任务的分子通信，重点关注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-解码器架构，高效提取、量化解码语义特征，通过优先处理任务相关语义信息以提升诊断分类性能。此外，引入概率信道网络来近似分子传播动力学，实现基于梯度的端到端学习优化。实验结果表明，在资源受限的通信场景下，相较于采用LDPC编码的传统JPEG压缩方法，所提出的语义框架将诊断准确率提升了至少25%。