Clustering-Guided Residual Neural Networks for Multi-Tx Localization in Molecular Communications
IEEE Communications Letters, cilt.30, ss.862-866, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 30
- Basım Tarihi: 2026
- Doi Numarası: 10.1109/lcomm.2025.3650145
- Dergi Adı: IEEE Communications Letters
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
- Sayfa Sayıları: ss.862-866
- Anahtar Kelimeler: Location awareness, Transmitters, Receivers, Estimation, Residual neural networks, Clustering algorithms, Accuracy, Molecular communication, Noise, Artificial intelligence, Multiple transmitter localization, molecular communications, residual neural networks
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Transmitter (Tx) 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 clustering-guided Residual Neural Networks, namely AngleNN and SizeNN. Experimental results show that both approaches provide significant improvements by reducing localization error between 69% (2-Tx) and 43% (4-Tx) compared to the K-means.