Integrating Model-Based and Data-Driven Detectors for Molecular MIMO Systems

Signal detection is a crucial process for communication systems, and molecular communication (MC) is no exception. Current research on signal detection in MC focuses on either model-based detectors or data-driven detectors. In contrast, our work investigates the integration of both schemes. By incorporating prior knowledge from the model-based scheme, the data-driven neural network structure can be redesigned, significantly mitigating the impacts of inter-symbol interference and inter-link interference in molecular multiple-input multiple-output (MIMO) systems. The feasibility of such an integrated detector is validated through experimental data from a molecular MIMO prototype, where channel state information is no longer indispensable. Moreover, numerical results demonstrate its superiority over conventional schemes in various aspects.