47. Deep Learning-Based Wireless Module Identification (WMI) Methods for Cognitive Wireless Communication Network

Nowadays, the internet of things (IoT) enabled event monitoring, controlling and managing systems employ different kinds of short range, medium-range and long-range wireless technologies. Wireless communication modules of most of the commercial IoT enabled automation systems operates in industrial, scientific and medical (ISM) frequency band. Therefore, identification of active wireless modules has become most essential to timely detect and track unauthorized users within the restricted zones. Further, it is also useful for finding density of wireless modules operating in the same and/or different frequencies with different and/or same communication protocols. In this paper, we attempt to present an automated wireless module identification (WMI) system based on the communication protocols of the three wireless modules, such as ZigBee, Bluetooth and Wi-Fi which are operated in the ISM frequency band. In this study, three WMI systems are developed based on deep learning networks (DLN), such as two-dimensional convolutional neural network (2D-CNN), long short-term memory (LSTM), and convolutional long short-term deep neural network (CLDNN). We evaluated three DLN based WMI methods on real-time RF signals recorded by using Blade RF, a software defined radio (SDR).