Neural Network With Attention Mechanism for Abnormality Detection and Localization in Diffusive Molecular Communication

Diffusive molecular communication (DMC) is an emerging paradigm in nanotechnology, which provides biocompatibility and nanoscale communication for many promising applications, such as targeted drug delivery, environmental monitoring, etc. However, detecting and localizing abnormalities in most of these applications is challenging, such as identifying tumor cells within the body or detecting pollution in air or water. In this paper, we introduce a method for detecting and localizing abnormalities in three dimensional DMC system with multiple sensors, receivers and one fusion center by adopting Transformer-based model with attention mechanism. We make full use of the attention mechanism to capture the inter-symbol interference (ISI) to improve the accuracy of detection and localization. In addition, we simplify the model structure to significantly reduce the complexity of this model. Furthermore, two strategies that different types of molecules (DMT) and same type of molecules (SMT) are released by sensors are considered. The training dataset and testing dataset are generated under these two strategies. Simulation results show that the information about the abnormality detection and localization can be obtained at the same time based on the Transformer-based model under DMT and SMT. Especially, our model outperforms the Informer-based model, deep neural networks (DNN)-based model and log-likelihood ratio (LLR) method.