Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis

TL;DR

This paper introduces a physics-based generative modeling approach for wireless channels that ensures geometric consistency and interpretability, addressing limitations of existing methods in realism and physical insight.

Contribution

It proposes a linearized reformulation of the PPGC model to improve generative training and provides a framework combining physics-based models with generative methods for realistic, interpretable wireless channel synthesis.

Findings

Generative models with the proposed reformulation outperform baselines in Wasserstein distance.

The approach yields scenario-specific, physically consistent channel samples.

Enhanced utility in downstream compression tasks.

Abstract

In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generative models have been proposed to synthesize channel matrices, but outputs produced by such methods may not correspond to geometrically viable channels and do not provide any insight into the scenario being generated. In this work, we aim to address both these issues by integrating established parametric, physics-based geometric channel (PPGC) modeling frameworks with generative methods to produce realistic channel matrices with interpretable representations in the parameter domain. We show that generative models converge to prohibitively suboptimal stationary points when learning the underlying prior directly over the parameters due to the non-convex PPGC model. To address this limitation, we propose a linearized reformulation of the problem to ensure smooth gradient flow during generative model training, while also providing insights into the underlying physical environment. We evaluate our model against prior baselines by comparing the generated, scenario-specific samples in terms of the 2-Wasserstein distance and through its utility when used for downstream compression tasks.