Base station (BS) deployment and operation are fundamental to network performance, yet they require accurate demand understanding, which remains difficult for operators. Cellular traffic in dense urban regions is well measured but highly dynamic, which undermines prediction-based management, whereas the scarcity of traffic measurements in emerging regions limits informed deployment decisions. Existing approaches therefore either depend on manual planning heuristics or use autoregressive predictors that fail to capture stochastic traffic variation. We present NetSpatial, a unified system for cellular planning and operation through spatially conditional traffic generation. NetSpatial exploits multimodal urban context, including satellite imagery and point of interest (POI) distributions, to learn how physical environment and functional semantics shape BS demand. It uses a multi-level flow-matching architecture that separates periodic structure from residual dynamics, enabling direct generation of long-horizon traffic sequences. NetSpatial supports two complementary decision scenarios, i.e., what-if analysis for deployment planning, which ranks candidate sites using generated traffic profiles, and what-to-do support for network operation, which uses generated traffic forecasts to guide BS sleep scheduling and load balancing. Experiments on real-world cellular traffic data show that NetSpatial reduces Jensen-Shannon Divergence (JSD) by 29.44% over the strongest baseline, generalizes across cities in zero-shot experiments, and enables up to 16.8% energy savings while maintaining over 80% quality of experience.

翻译：基站部署与运维是网络性能的基础，但需要准确的需求理解，这对运营商而言仍然困难。密集城区的蜂窝流量测量充分但高度动态，这削弱了基于预测的管理效果；而新兴区域流量测量的稀缺性限制了有依据的部署决策。现有方法因此要么依赖人工规划启发式规则，要么使用无法捕捉随机流量变化的自回归预测器。我们提出NetSpatial，一个通过空间条件流量生成实现蜂窝网络规划与运维的统一系统。NetSpatial利用多模态城市上下文（包括卫星影像和兴趣点分布）来学习物理环境与功能语义如何塑造基站需求。它采用一种分离周期性结构与残差动态的多层级流匹配架构，能够直接生成长时程流量序列。NetSpatial支持两种互补的决策场景：面向部署规划的假设分析（使用生成的流量特征对候选站点进行排序），以及面向网络运维的决策支持（使用生成的流量预测指导基站休眠调度与负载均衡）。在真实蜂窝流量数据上的实验表明，NetSpatial将Jensen-Shannon散度较最强基线降低了29.44%，在零样本实验中能跨城市泛化，并在保持80%以上体验质量的同时实现高达16.8%的节能效果。