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 fourthgeneration 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.

Reliable analysis of the electromagnetic situation in a particular area is critical for wireless technology applications. With the increased number of devices and exploration of the new frequency bands, sensing helps, inter alia, in interference and radio resource management. Additionally, it facilitates the classification of signals to detect jammers and distinguish between legal and anomalous transmitters. This paper discusses the cooperative sensor network as a technology candidate for 6G. Specifically, we describe a possible architectural extension of the future cellular networks to obtain and utilize spectrum awareness. Several applications are systematically reviewed in this context, including dynamic spectrum sharing for coexistence in unlicensed bands, spectrum monitoring for signal detection and classification, and emitter localization. Qualitative and quantitative examples of possible performance improvements in cellular networks with the proposed approach are presented. Finally, we highlight the open research topics and challenges to solve for such a cooperative sensor network.