NetSpatial: Spatially Conditional Traffic Generation for Cellular Planning and Operations

2026-03-14

AI Summary

This work addresses the challenge of accurately modeling dynamic and spatially heterogeneous cellular traffic—particularly in emerging regions with scarce data—where conventional base station deployment and operation strategies fall short. We propose NetSpatial, the first system to integrate spatial conditional generative models into cellular traffic forecasting. By fusing multimodal urban context such as satellite imagery and points of interest, and employing a multi-level flow-matching architecture that decouples periodic patterns from residual dynamics, NetSpatial enables direct long-horizon traffic generation. The framework unifies “what-if” analysis for deployment planning and “what-to-do” optimization for operational decisions. Evaluated on real-world data, it reduces Jensen–Shannon divergence by 29.44%, demonstrates zero-shot generalization across cities, and achieves 16.8% energy savings through base station sleep scheduling and load balancing while maintaining quality of experience for over 80% of users.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Generation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• cellular traffic generation
• base station deployment
• spatially conditional modeling
• network planning
• traffic demand prediction

Innovation

• spatially conditional generation
• multi-level flow-matching
• multimodal urban context
• cellular traffic forecasting
• zero-shot generalization