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 reexamining 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%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.