LSTM-DNN-Based Joint Wireless Signal Waveform Classification and Blind Transmitter Localization

• Published in: 2024 19th International Symposium on Wireless Communication Systems (ISWCS) pp. 1 - 6
• Main Authors: Shatov, Victor, Shanin, Nikita, Perner, Bastian, Su, Yanpeng, Franchi, Norman, Lubke, Maximilian
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.07.2024
• Subjects: blind transmitter localization; deep neural network (DNN); Location awareness; long short-term memory (LSTM); Monitoring; multifunctional sensor network; Radio transmitters; Reliability; signal waveform classification; spectrum monitoring; Task analysis; Wireless communication; wireless sensor network (WSN); Wireless sensor networks
• ISSN: 2154-0225
• DOI: 10.1109/ISWCS61526.2024.10639099

The steadily increasing number of wireless Internet of Things devices imposes new challenges on spectrum monitoring and spectrum management tasks. In this paper, we design a multifunctional cooperative sensor network to analyze electromagnetic (EM) situation in the monitored area. Specifically, we introduce a framework based on a long short-term memory (LSTM) deep neural network (DNN) to jointly localize a single transmitter (TX) and classify the waveform of the TX signal. The performance capability of separating fifth-generation New Radio (5G NR) communications standard signals from fourth-generation Long Term Evolution (4G LTE) communications standard signals and Wi-Fi signals is investigated as a classification problem. Furthermore, the associated TX localization accuracy is measured regarding mean squared error (MSE). We show that in a basic scenario, it is possible to yield a reasonable TX localization performance without any prior knowledge of the signals' structure with just four sensors, in parallel getting reliable wireless waveform classification. Finally, we suggest future research directions for joint signal classification and TX localization in cooperative sensor networks.