Demystifying Molecular Data-Driven Detection With Explainable Artificial Intelligence

Abstract

Molecular communication (MC) is a bio-inspired paradigm for information exchange that leverages the properties of messenger molecules for data transmission. Recognized as a promising physical-layer technique for the Internet of Bio-Nano Things within biological entities, MC facilitates intricate collaboration and networking among micro-scale devices. Data-driven detectors are favored in MC receivers due to their complex and dynamic channel characteristics. The messages of the MC systems are vital, while the recovery process via the data-driven detectors mostly exhibits an opaque nature. To address this transparency issue, this letter uses an artificial intelligence tools, SHapley Additive exPlanations (SHAP), to explain the basic principles of data-driven detectors in MC from a systematic perspective. Through this approach, important feature points of the received signals are extracted, which further enhances the detection performance of MC with a reduced need for signal samples, thereby substantiating the role of interpretability in improving the functional capabilities of MC systems.