Dual-Task Supervised Learning for Distributed Spectrum Monitoring Applications

This paper examines a novel data-driven framework for spectrum monitoring in the context of non-public networks. To this end, we model an indoor scenario with a single non-cooperative transmitter (TX) and a spatially distributed network of sensor units (SUs) capable of recording the broadcasted signal as I/Q samples. Then, a dual-task deep neural network (DNN) is designed to jointly classify wireless signal waveform and estimate the TX coordinates, using raw data from the SUs as input. The numerical simulations focus on the model's scalability, generalizability, and robustness. In this context, we study the effect of the number of SUs and varying signal-to-noise ratio (SNR) on the accuracy of the developed DNN-based joint signal classification and TX localization algorithm. In particular, we show that an increased number of SUs improves the localization accuracy, whereas the performance of waveform classification remains nearly unchanged. Finally, we assess the model's complexity regarding the number of learnable parameters compared to single-task DNNs, demonstrating that extra functionality can be obtained at a low additional complexity cost.