用视频+信号+几何特征自动识别基站天线归属，提升通信网络维护效率。
Token Entropy Regularization for Multi-modal Antenna Affiliation Identification
- 融合视频、信号与几何特征，将天线归属转为多模态分类任务。
- 提出令牌熵正则化模块，使模型收敛更快，性能显著提升。
- 适合通信网络自动化运维、多模态学习研究者参考。
准确的天线归属识别对优化和维护通信网络至关重要。当前方法依赖繁琐且易出错的手动塔站巡检。本文提出一种新范式，融合基站视频、天线几何特征与物理小区标识（PCI）信号，将天线归属识别转化为多模态分类与匹配任务。公开可用的预训练变换器因缺乏通信领域的类似数据，难以实现跨模态对齐。为此，我们设计了专用训练框架，对齐天线图像与对应PCI信号。针对表示对齐挑战，提出一种新型令牌熵正则化（TER）模块，应用于预训练阶段。实验表明，TER加速收敛并带来显著性能提升。进一步分析显示，首个令牌的熵值具有模态依赖性。代码将在发表后公开。
原文摘要 · Abstract (English)
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.
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