Open Access

ARTICLE

Deep Learning-based Wireless Signal Classification in the IoT Environment

Hyeji Roh, Sheungmin Oh, Hajun Song, Jinseo Han, Sangsoon Lim^*
Department of Computer Engineering, Sungkyul University, Anyang, 430742, Korea
* Corresponding Author: Sangsoon Lim. Email:

Computers, Materials & Continua 2022, 71(3), 5717-5732. https://doi.org/10.32604/cmc.2022.024135

Received 06 October 2021; Accepted 06 December 2021; Issue published 14 January 2022

Abstract

With the development of the Internet of Things (IoT), diverse wireless devices are increasing rapidly. Those devices have different wireless interfaces that generate incompatible wireless signals. Each signal has its own physical characteristics with signal modulation and demodulation scheme. When there exist different wireless devices, they can suffer from severe Cross-Technology Interferences (CTI). To reduce the communication overhead due to the CTI in the real IoT environment, a central coordinator can be able to detect and identify wireless signals existing in the same communication areas. This paper investigates how to classify various radio signals using Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and attention mechanism. CNN can reduce the amount of computation by reducing weights by using convolution, and LSTM belonging to RNN models can alleviate the long-term dependence problem. Furthermore, attention mechanism can reduce the short-term memory problem of RNNs by re-examining the data output from the decoder and the entire data entered into the encoder at every point in time. To accurately classify radio signals according to their weights, we design a model based on CNN, LSTM, and attention mechanism. As a result, we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences. In a case of the real IoT environment with Wi-Fi, Bluetooth and ZigBee devices, we can normally obtain wireless signals from 10 to 20 dB. The accuracy of CLARINet's radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio (SNR) exhibits high accuracy at 16 dB to about 92.03%.

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Keywords

Attention mechanism; wireless signal; CNN-LSTM; classification; deep-learning

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