Token Entropy Regularization for Multi-modal Antenna Affiliation Identification

2026-01-29

AI Summary

This work addresses the inefficiency and error-proneness of manual inspection in identifying antenna affiliations within communication networks. To overcome these limitations, we propose a multimodal classification and matching approach that integrates base station video, antenna geometric features, and Physical Cell Identity (PCI) signals. Building upon a pretrained Transformer architecture, our method introduces a novel Token Entropy Regularization module that promotes cross-modal alignment between visual and PCI modalities during pretraining by regulating the entropy of the first token. This strategy effectively mitigates alignment challenges caused by the scarcity of class labels in the telecommunications domain. Experimental results demonstrate that the proposed approach significantly accelerates model convergence and improves identification accuracy, while also revealing the modality-dependent nature of the first-token entropy.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Accurate antenna affiliation identification is crucial for optimizing and maintaining communication networks. Current practice, however, relies on the cumbersome and error-prone process of manual tower inspections. We propose a novel paradigm shift that fuses video footage of base stations, antenna geometric features, and Physical Cell Identity (PCI) signals, transforming antenna affiliation identification into multi-modal classification and matching tasks. Publicly available pretrained transformers struggle with this unique task due to a lack of analogous data in the communications domain, which hampers cross-modal alignment. To address this, we introduce a dedicated training framework that aligns antenna images with corresponding PCI signals. To tackle the representation alignment challenge, we propose a novel Token Entropy Regularization module in the pretraining stage. Our experiments demonstrate that TER accelerates convergence and yields significant performance gains. Further analysis reveals that the entropy of the first token is modality-dependent. Code will be made available upon publication.

Problem

• antenna affiliation identification
• multi-modal classification
• cross-modal alignment
• communication networks
• PCI signals

Innovation

• Token Entropy Regularization
• multi-modal alignment
• antenna affiliation identification
• PCI signal
• pretrained transformers