Automated Angular Received-Power Characterization of Embedded mmWave Transmitters Using Geometry-Calibrated Spatial Sampling
Abstract
This paper presents an automated measurement methodology for angular received-power characterization of embedded millimeter-wave transmitters using geometry-calibrated spatial sampling. Characterization of integrated mmWave transmitters remains challenging due to limited angular coverage and alignment variability in conventional probe-station techniques, as well as the impracticality of anechoic-chamber testing for platform-mounted active modules. To address these challenges, we introduce RAPTAR, an autonomous measurement system for angular received-power acquisition under realistic installation constraints. A collaborative robot executes geometry-calibrated, collision-aware hemispherical trajectories while carrying a calibrated receive probe, enabling controlled and repeatable spatial positioning around a fixed device under test. A spectrum-analyzer-based receiver chain acquires amplitude-only received power as a function of angle and distance following quasi-static pose stabilization. The proposed framework enables repeatable angular received-power mapping and power-domain comparison against idealized free-space references derived from full-wave simulation. Experimental results for a 60-GHz radar module demonstrate a mean absolute received-power error below 2 dB relative to simulation-derived references and a 36.5 % reduction in error compared to manual probe-station measurements, attributed primarily to reduced alignment variability and consistent spatial sampling. The proposed method eliminates the need for coherent field measurements and near-field transformations, enabling practical power-domain characterization of embedded mmWave modules. It is well suited for angular validation in real-world platforms where conventional anechoic measurements are impractical.
Cite
@article{arxiv.2601.12562,
title = {Automated Angular Received-Power Characterization of Embedded mmWave Transmitters Using Geometry-Calibrated Spatial Sampling},
author = {Maaz Qureshi and Mohammad Omid Bagheri and Abdelrahman Elbadrawy and William Melek and George Shaker},
journal= {arXiv preprint arXiv:2601.12562},
year = {2026}
}