TeRFS: Temporal-Evolving Radio Field Synthesis

TL;DR

TeRFS introduces a novel method for dynamic RF field synthesis that models multipath evolution over time, outperforming existing approaches in accuracy and speed, especially in highly mobile environments.

Contribution

It proposes a new temporal-evolving synthesis framework using anisotropic spherical Gaussian bases with a birth-and-death mechanism for path lifecycle modeling.

Findings

Achieves 11.5% lower MSE than SOTA methods.

Provides 6.9x faster training speed.

Maintains robust tracking with median error of 1.52 dB in dynamic scenarios.

Abstract

While radio-frequency (RF) field synthesis is fundamental to wireless networking, current approaches remain constrained by static assumptions, leaving them unable to track the rapid multipath reorganization of dynamic scenes. Modeling these transitions requires addressing two coupled challenges: explicit temporal representation and the capture of discrete path lifecycles. To bridge this gap, Temporal-Evolving Radio Field Synthesis (TeRFS) is introduced. TeRFS utilizes an anisotropic spherical Gaussian (ASG) directional basis to represent sparse, sharp angular structures, bound to analytical temporal envelopes that regulate path lifecycles. This formulation induces a mathematical birth-and-death mechanism, enabling individual multipath trajectories to emerge and vanish with temporal precision, a capability beyond the reach of standard smooth interpolation. Evaluations demonstrate that TeRFS outperforms state-of-the-art (SOTA) baselines, achieving an 11.5% reduction in mean squared error (MSE) alongside a 6.9 times training speedup. Even in environments characterized by extreme structural mutation, TeRFS maintains robust tracking of dynamic reorganizations, limiting median absolute error to 1.52 dB and establishing its utility for high-mobility wireless applications.