Two Measure is Two Know: Calibration-free Full Duplex Monitoring for Software Radio Platforms

TL;DR

FDMonitor is a calibration-free, real-time full-duplex monitoring system for software radio platforms that accurately detects spectrum violations without prior calibration, demonstrated over seven months on a real testbed.

Contribution

This paper introduces FDMonitor, a novel calibration-free full-duplex monitoring system with adaptive source separation for software radio platforms.

Findings

Achieves 97% positive predictive value in real-world tests.

Effectively separates signals across various parameters.

Operates reliably over seven months with minimal false alerts.

Abstract

Future virtualized radio access network (vRAN) infrastructure providers (and today's experimental wireless testbed providers) may be simultaneously uncertain what signals are being transmitted by their base stations and legally responsible for their violations. These providers must monitor the spectrum of transmissions and external signals without access to the radio itself. In this paper, we propose FDMonitor, a full-duplex monitoring system attached between a transmitter and its antenna to achieve this goal. Measuring the signal at this point on the RF path is necessary but insufficient since the antenna is a bidirectional device. FDMonitor thus uses a bidirectional coupler, a two-channel receiver, and a new source separation algorithm to simultaneously estimate the transmitted signal and the signal incident on the antenna. Rather than requiring an offline calibration, we also adaptively estimate the linear model for the system on the fly. FDMonitor has been running on a real-world open wireless testbed, monitoring 19 SDR platforms controlled (with bare metal access) by outside experimenters over a seven month period, sending alerts whenever a violation is observed. Our experimental results show that FDMonitor accurately separates signals across a range of signal parameters. Over more than 7 months of observation, it achieves a positive predictive value of 97%, with a total of 20 false alerts.