Clustering Guided Residual Neural Networks for Multi-Tx Localization in Molecular Communications
Ali Sonmez, Erencem Ozbey, Efe Feyzi Mantaroglu, H. Birkan Yilmaz
通过扩散在分子通信中的发射器定位是具有许多应用的关键主题。 然而,由于接收器表面扩散和分子分布重叠的随机性,多个发射器的精确定位是一个具有挑战性的问题。 为了解决这些问题,我们引入了基于聚类的中心校正方法,可增强对密度变化的鲁棒性和异常值。 此外,我们提出了两个聚类引导残余神经网络,即用于方向改进的AngleNN和用于集群大小估计的SizeNN。 实验结果表明,与K-means相比,这两种方法都提供了显着的改进,减少了69%(2-Tx)和43%(4-Tx)之间的定位误差。
Transmitter localization in Molecular Communication via Diffusion is a critical topic with many applications. However, accurate localization of multiple transmitters is a challenging problem due to the stochastic nature of diffusion and overlapping molecule distributions at the receiver surface. To address these issues, we introduce clustering-based centroid correction methods that enhance robustness against density variations, and outliers. In addition, we propose two clusteringguided Residual ...