用聚类引导的神经网络提升分子通信中多发射源定位精度
Clustering Guided Residual Neural Networks for Multi-Tx Localization in Molecular Communications
- 通过聚类修正中心点，增强对分子密度变化和异常值的鲁棒性
- AngleNN与SizeNN分别优化方向与簇大小，使定位误差降低43%~69%
- 适合研究分子通信、信号处理或智能算法的科研人员参考
在扩散型分子通信中，发射源定位是一个关键问题，具有广泛应用价值。然而，由于扩散过程的随机性以及接收端表面分子分布重叠，多个发射源的精确定位面临挑战。为此，本文提出基于聚类的中心点修正方法，以增强对分子密度变化和异常值的鲁棒性。同时，设计了两种聚类引导的残差神经网络：AngleNN用于方向精修，SizeNN用于簇大小估计。实验结果表明，相较于K-means方法，该方案在2个发射源场景下定位误差降低69%，在4个发射源场景下降低43%，显著提升定位性能。
原文摘要 · Abstract (English)
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 Neural Networks, namely AngleNN for direction refinement and SizeNN for cluster size estimation. Experimental results show that both approaches provide significant improvements with reducing localization error between 69% (2-Tx) and 43% (4-Tx) compared to the K-means.
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