CITYMPC: A Large-Scale Physics-Informed Benchmark and Tool for Generative Complete Multipath Wireless Channel Modeling

TL;DR

CITYMPC is a physics-informed generative model that predicts detailed multipath wireless channel parameters from imagery and terrain data, matching ray tracing accuracy without needing 3D scene geometry.

Contribution

It introduces CITYMPC, a novel conditional variational autoencoder that generates environment-aware wireless channels from 2D data, and provides a large-scale benchmark dataset for future research.

Findings

CITYMPC achieves 1.29 dB MAE in received power prediction.

It attains 7.25 ns MAE in delay estimation.

The framework is validated across five urban environments with over 427,000 links.

Abstract

Multipath wireless channels are fully characterized by multipath components (MPCs), including complex channel gain, propagation delay, angle of departure (AoD) and angle of arrival (AoA) in azimuth and elevation. Generating these parameters with the fidelity of ray tracing (RT) remains an open problem. Existing methods either incur the computational cost of RT or require explicit 3D scene geometry at inference. We present CITYMPC, a conditional variational autoencoder (cVAE) that predicts the complete per-path MPC parameter set from point-of-view imagery and terrain height maps alone, achieving environment-aware channel generation without access to any three-dimensional scene geometry at inference. Trained and evaluated across five urban environments spanning 427,397 links, CITYMPC matches RT ground truth to within 1.29 dB received power mean absolute error (MAE) and 7.25 ns $\tau_0$ MAE. CITYMPC is a generative channel modeling framework and reproducible benchmark, released together with a large-scale multi-city ray-traced dataset to accelerate future scene-conditioned channel modeling research. We further analyze cross-city distribution shift to characterize the per-city diversity of the benchmark.