Building Semantic Communication System via Molecules: An End-to-End Training Approach
This work addresses communication efficiency for scenarios with limited resources, such as molecular networks, but is incremental as it applies existing semantic communication concepts to a specific domain.
The paper tackles the problem of inefficient molecular communication by proposing an end-to-end semantic system that encodes task-relevant information into molecule concentrations, resulting in superior performance over conventional methods in classification tasks.
The concept of semantic communication provides a novel approach for applications in scenarios with limited communication resources. In this paper, we propose an end-to-end (E2E) semantic molecular communication system, aiming to enhance the efficiency of molecular communication systems by reducing the transmitted information. Specifically, following the joint source channel coding paradigm, the network is designed to encode the task-relevant information into the concentration of the information molecules, which is robust to the degradation of the molecular communication channel. Furthermore, we propose a channel network to enable the E2E learning over the non-differentiable molecular channel. Experimental results demonstrate the superior performance of the semantic molecular communication system over the conventional methods in classification tasks.