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Deep learning aided wireless interference identification for coexistence management in the ISM bands

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Abstract

Innovative wireless technology trends and applications have attracted an incredible number of users resulting in the massive utilization of the unlicensed ISM bands. In order to accommodate the existing ISM users and to ensure optimal spectrum sharing amongst heterogeneous wireless technologies, spectrum awareness would be of keen importance in identifying concurrent transmission and subsequently applying suitable interference mitigation techniques to ensure coexistence and prevent communication blackout. Our work uses deep learning to identify the presence of WSN, WiFi and Bluetooth single-label signals. Furthermore, we aim to identify multi-label concurrent signal transmissions that are significant in the context of interference management. The experimental results show that the proposed approach yields an average classification accuracy of up to 99.9% and 99.4% for single-label and multi-label signal classification respectively. Moreover, we investigated the effect of data representations (raw data and image data), time–frequency signal representations (spectrogram and Fourier synchrosqueezed transform), color space (RGB images and grayscale images) and various deep learning models for signal classification.

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Acknowledgements

The authors would like to acknowledge Schmidt et al.[21] for sharing the CRAWDAD owl/interference dataset.

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  1. Department of Electrical Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan

    Ayesha Hasan & Bilal Muhammad Khan

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  1. Ayesha Hasan
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Glossary

WSN

Wireless sensor networks

Single-label signal

Radio samples in which one signal is present at a time

Multi-label signal/Mixture Signal

Radio samples in which multiple concurrent signals are present at a time

WII

Wireless interference identification

WTR

Wireless technology recognition

TFR

Time–frequency representation/spectral plot

STFT

Short Time fourier transform

Spectrogram

STFT based spectral plot

FSST

Fourier synchrosqueezing transform

CNN

Convolutional neural networks

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Hasan, A., Khan, B.M. Deep learning aided wireless interference identification for coexistence management in the ISM bands. Wireless Netw 29, 3311–3331 (2023). https://doi.org/10.1007/s11276-023-03389-3

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