A Foundation for Wireless Channel Prediction and Full Ray Makeup Estimation Using an Unmanned Vehicle

TL;DR

This paper introduces a novel framework for wireless channel prediction using an unmanned vehicle, capable of estimating detailed ray parameters and channel quality at unvisited locations, validated through extensive real-world experiments.

Contribution

A new method for predicting detailed wireless ray makeup and channel quality at unvisited locations using minimal prior measurements and robotic route design.

Findings

Accurately predicts received power and ray makeup at unvisited locations.

Outperforms state-of-the-art wireless channel prediction methods.

Validated through extensive real-world experiments in multiple areas.

Abstract

In this paper, we consider the problem of wireless channel prediction, where we are interested in predicting the channel quality at unvisited locations in an area of interest, based on a small number of prior received power measurements collected by an unmanned vehicle in the area. We propose a new framework for channel prediction that can not only predict the detailed variations of the received power, but can also predict the detailed makeup of the wireless rays (i.e., amplitude, angle-of-arrival, and phase of all the incoming paths). More specifically, we show how an enclosure-based robotic route design ensures that the received power measurements at the prior measurement locations can be utilized to fully predict detailed ray parameters at unvisited locations. We then show how to first estimate the detailed ray parameters at the prior measurement route and then fully extend them to predict the detailed ray makeup at unvisited locations in the workspace. We experimentally validate our proposed framework through extensive real-world experiments in three different areas, and show that our approach can accurately predict the received channel power and the detailed makeup of the rays at unvisited locations in an area, considerably outperforming the state-of-the-art in wireless channel prediction.