NetSpatial: Spatially Conditional Traffic Generation for Cellular Planning and Operations

TL;DR

NetSpatial is a system that uses urban context data to generate realistic cellular traffic patterns, aiding network planning and operation with improved accuracy and energy efficiency.

Contribution

It introduces a novel spatially conditional traffic generation model that captures stochastic traffic variations for cellular network planning and management.

Findings

Reduces Jensen-Shannon Divergence by 29.44% compared to baselines.

Generalizes across cities in zero-shot experiments.

Enables up to 16.8% energy savings while maintaining quality.

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.