发表机构
Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; Sun Yat-Sen University, Shenzhen; Shenzhen Loop Area Institute（深圳大数据研究院; 香港中文大学（深圳）; 中山大学（深圳）; 深圳河套学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出ChaRT基础模型，利用运营网络测量报告学习可迁移无线电表示，实现跨城市无线电环境重建及多任务迁移，仅需1%标记数据即可支持定位、波束预测等任务。
AI 中文摘要
无线蜂窝网络为通信、交通和工业提供关键基础设施，使可靠的连接对现代社会至关重要。提供这种连接需要对由网络基础设施及其周围环境共同塑造的无线电环境进行精确建模。此类模型支撑基站部署、网络运营和参数优化，然而城市规模的无线电环境仍然难以捕捉。基于物理的工具需要详细的站点描述和计算，而特定任务的模型需要专门的测量，且在不同部署之间迁移能力差。在此，我们介绍ChaRT，一个从运营蜂窝网络常规生成的测量报告中学习可迁移无线电表示的基础模型。这些报告在无需额外测量活动的情况下，提供跨多个小区和波束的丰富联合观测。ChaRT将波束级角度结构、网络层次和传播机制多样性纳入其架构，并通过上下文感知的掩码波束建模和自蒸馏进行预训练。我们在一个城市中来自3503个小区的超过10亿份报告（包含182亿个波束级观测）上训练ChaRT。凭借单一权重集，ChaRT能够重建未见城市的无线电环境，并迁移到新站点预测、无线电地图构建和网络参数调优。仅使用1%的标记数据，它就能支持用户定位、波束预测、传播场景分类以及信号与干扰加噪声比的估计。学习到的表示还支持用于可复用无线电网格构建的波束空间聚类。这些结果确立了运营测量报告作为可迁移蜂窝网络智能的可扩展数据基础。
英文摘要
Wireless cellular networks form the connective tissue of human society, sustained by a continuous physical dialogue between engineered infrastructure and its surroundings. Radio signals emitted from base stations traverse terrain, diffract around buildings and scatter through streets before reaching billions of users. Together, these interactions produce the city-wide radio environment on which every network decision rests. Shaping this environment through deployment and optimization determines the connectivity societies rely on, yet learning it effectively at city scale and generalizing across diverse cities and deployments remain open challenges. Here we answer positively by introducing ChaRT, a foundation model that learns transferable radio representations from measurement reports generated by deployed cellular networks. These reports provide abundant multi-cell, multi-beam observations without dedicated campaigns, forming a scalable data foundation for city-scale learning. ChaRT embeds beam-level angular structure, network hierarchy and propagation-regime diversity in its architecture, and is pretrained through context-aware masked beam modelling and self-distillation with channel-model-constrained augmentation. We pretrain ChaRT on over one billion reports comprising 18.2 billion beam-level observations from 3,503 cells in one city. With a single set of weights, ChaRT reconstructs radio environments in unseen cities and transfers to radio map construction, new-site prediction and network parameter tuning. With only 1% of labelled data, it supports user localization, beam prediction, propagation scenario classification and estimation of the signal-to-interference-plus-noise ratio. The learned representation further enables beamspace clustering for reusable radio-grid construction. These results establish ChaRT as a transferable foundation for network-wide intelligence.
