Abstract
A growing research agenda to identify coexisting wireless technologies is focusing mainly on matching significant advances in wireless technologies with the ever-increasing spectrum demand for the Internet of Things (IoT), including activity in the license-free Industrial, Scientific, and Medical (ISM) bands. This paper presents a novel algorithm based on dynamic mode decomposition (DMD) modeling for detecting and differentiating various IEEE 802.11x Wi-Fi standard signals using collected timeseries raw power measurements. Wi-Fi signals were collected across 802.11n, 802.11ac, and 802.11ax standards operating in the 5GHz ISM band under individual (no sharing) and multiple coexisted technologies. The proposed algorithm identified the time domain signature of a signal by capturing embedded periodicity features transmitted within the signal. A major advantage of the technique is its minimal requirements-no data preprocessing, no channel estimation, no time/frequency synchronization, and no need for long observation time intervals. Method accuracy for targeted signals was evaluated and is reported herein.
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