Wireless Technology Identification Employing Dynamic Mode Decomposition Modeling

Ahmed Elsebaay

doi:10.1109/ACCESS.2023.3247519

2023-01-01

Ahmed Elsebaay *

Hazem H. Refai

DOI：10.1109/ACCESS.2023.3247519

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摘要

En 中文

Significant growth in broadband wireless services, as well as ever-increasing demand on the spectrum caused by the Internet of Things (IoT) have overstretched limited available spectrum space for wireless services. Heterogeneous wireless networks (HetNets)-wherein multiple wireless technologies (e.g., Wi-Fi, Bluetooth, Zigbee, LTE, and GSM) coexist and share spectrum-are a promising solution for enhancing spectrum sharing. An essential element in developing coexistence protocols is correctly identifying wireless technologies anticipated to share spectrum and to shift users between available wireless technologies in an effort to optimize spectrum usage and minimize interference. For the coexistence research reported in this paper, we analyzed the performance of our developed novel algorithm based on dynamic mode decomposition (DMD) mathematical modeling to identify and differentiate among various wireless technologies. More specifically, our technique identified GSM and LTE signals in the cellular domain, IEEE802.11n, ac, and ax in the Wi-Fi domain, as well as Bluetooth and Zigbee. The proposed DMD-based technique identifies the time domain signature of a signal by capturing embedded periodic features transmitted within the signal. Performance and accuracy were tested and validated using an experimental dataset collected for various time series, and raw-power measurements of the targeted technologies. Results showed that the developed DMD-based algorithm can differentiate and classify individual and coexisting wireless signals with high accuracy -greater than 90% for most cases. Furthermore, only a short time- less than one second-is required for identifying a signal and enabling implementation in real-time practical networks. The advantage of the developed technique over comparable techniques is lower complexity (i.e., shorter processing and training time, no channel estimation, no time/frequency synchronization, and no need for long observation-time intervals).

Keyword:

Wireless communication

Signal to noise ratio

Classification algorithms

Long Term Evolution

GSM

Convolutional neural networks

Mathematical models

Dynamic mode decomposition

wireless coexistence

wireless identification