Comparing Stochastic and Ray-tracing Datasets in Machine Learning for Wireless Applications

TL;DR

This paper compares the effectiveness of stochastic channel models and ray-traced data in machine learning tasks for wireless systems, highlighting when each approach is appropriate based on empirical results.

Contribution

It provides an empirical analysis of stochastic versus ray-traced datasets for wireless ML tasks, offering guidelines for model selection and benchmarking.

Findings

Stochastic models may misestimate performance compared to RT data.

RT data benefits tasks requiring strong spatiotemporal understanding.

A task-aware approach improves model evaluation and pre-training strategies.

Abstract

Machine learning for wireless systems is commonly studied using standardized stochastic channel models (e.g., TDL/CDL/UMa) because of their legacy in wireless communication standardization and their ability to generate data at scale. However, some of their structural assumptions may diverge from real-world propagation. This paper asks when these models are sufficient and when ray-traced (RT) data - a proxy for the real world - provides tangible benefits. To answer these questions, we conduct an empirical study on two representative tasks: CSI compression and temporal channel prediction. Models are trained and evaluated using in-domain, cross-domain, and small-data fine-tuning protocols. Across settings, we observe that stochastic-only evaluation may over- or under-estimate performance relative to RT. These findings support a task-aware recipe where stochastic models can be leveraged for scalable pre-training and for tasks that do not rely on strong spatiotemporal coupling. When that coupling matters, pre-training and evaluation should be grounded in spatially consistent or geometrically similar RT scenarios. This study provides initial guidance to inform future discussions on benchmarking and standardization.