Clustering Guided Residual Neural Networks for Multi-Tx Localization in Molecular Communications

The recent study on transmitter localization in molecular communications via diffusion presents innovative clustering-based centroid correction methods aimed at overcoming the inherent challenges of stochastic diffusion and overlapping molecule distributions. The introduction of two novel models, AngleNN for direction refinement and SizeNN for estimating cluster sizes, has shown promising results. Experimental findings indicate that these methods significantly reduce localization errors, achieving reductions of 69% for two transmitters and 43% for four transmitters when compared to the conventional K-means approach. This advancement is crucial as accurate localization is vital for various applications in molecular communications, emphasizing the importance of ongoing research in this field.