Optimizing Performance of Molecular Communication System using Congestion Control Mechanisms

In molecular communication (MC), efficient transport of information-carrying molecules between the transmitter and receiver is essential for applications such as targeted drug delivery. A major challenge is achieving high transmission rates while avoiding receiver congestion, which can lead to molecule loss and reduced system efficiency. This study proposes a congestion control framework that dynamically regulates molecule emission rates based on receiver congestion conditions to maximize throughput and minimize inefficiencies. The proposed framework is evaluated using computational modeling and simulations in COMSOL Multiphysics, considering five congestion-control strategies: fixed-threshold control, adaptive thresholding, controlled threshold adjustment, predictive–model–based control, and a TCP TAHOE–inspired state-transition mechanism. Each strategy is systematically analyzed in terms of throughput optimization and congestion mitigation. Simulation results show that adaptive strategies significantly outperform static threshold-based methods by providing improved responsiveness and robustness under varying channel conditions. The findings offer practical design insights for efficient MC systems, particularly in therapeutic drug delivery applications where precise molecular flow regulation is required. By addressing key congestion management challenges, this work contributes to the advancement of molecular communication and supports its applicability to emerging nanoscale and biomedical systems.