Platform-Aware Channel Knowledge Mapping via Mutual Antenna Pattern Learning in 3D Wireless Links

TL;DR

This paper introduces a platform-aware framework that models wireless link characteristics through a novel mutual antenna pattern, effectively capturing platform-induced scattering and reflections to improve path loss estimation.

Contribution

It presents a new mutual antenna pattern model that jointly characterizes AoA and AoD effects, enabling more accurate wireless link modeling considering hardware mounting structures.

Findings

Mutual antenna pattern can be estimated with as few as 10 measurements per angular bin.

The learned pattern reduces path loss estimation errors by up to 10 dB.

The framework is validated with experimental data showing improved accuracy.

Abstract

This letter proposes a platform-aware framework to characterize wireless links by empirically modeling the `near-platform' scattering and reflections induced by the hardware mounting structures of both endpoints. We model the link characteristics as a novel mutual antenna pattern: a joint function of the angle of arrival (AoA) and angle of departure (AoD). We demonstrate that while individual platform-aware patterns are mathematically unidentifiable from power measurements, the coupled mutual pattern can be effectively estimated in a least-squares sense. Our framework is evaluated using noisy measurement data, revealing that as few as 10 measurements per joint-angular bin are sufficient. The proposed methodology is validated through cross-validation of experimental subsets, demonstrating that the learned mutual radiation pattern reduces path loss estimation errors by up to 10 dB compared to traditional models using isolated anechoic chamber antenna gains.