Statistical Model of Time-varying Backscatter Power of Monostatic RF Sensing Channels in Urban Canyons

TL;DR

This paper develops a statistical model for the time-varying backscatter power in urban RF sensing channels, validated through measurements at 140 GHz in city environments, aiding large-scale 6G system evaluation.

Contribution

It introduces a measurement-based statistical model that captures backscatter power variations without detailed environmental data, suitable for system-level performance analysis.

Findings

Deterministic model reproduces average backscattered power with 3.3 dB RMS error.

Azimuthal power variation modeled within 0.5 dB using a lognormal distribution.

Temporal fluctuations follow a Rician distribution with a lognormal K-factor.

Abstract

We present a measurement-based statistical model for the backscatter power ratio of monostatic RF sensing in urban canyons with moving clutter, suitable for large-scale system level performance evaluation of RF sensing in 6G networks. A narrowband (CW) 140 GHz sounder used a monostatic radar arrangement with an omnidirectional transmit antenna illuminating streets and a spinning horn 2o receive antenna offset vertically (less than 1 m away) collecting backscattered power as a function of azimuth and time below building height in Manhattan and Valparaiso, Chile. A concise outdoor deterministic model of average backscattered power dependent on distance to nearest building-wall reproduces observations with 3.3 dB RMS error or better. Distribution of power variation in azimuth around this average is reproduced within 0.5 dB by a random azimuth spectrum with a lognormal distribution. Temporal fluctuations for various antenna aims and locations were found to be well modeled by a Rician distribution, with lognormally distributed K-factor, with 0.47-0.73 correlation coefficient to backscatter power deviation from mean. The statistical model does not require a detailed environmental description, aiming to reproduce backscatter clutter statistics (as opposed to a deterministic response) faithfully and efficiently, essential for large-scale system-level performance evaluation.