  Future communication networks must address the scarce spectrum to accommodate
extensive growth of heterogeneous wireless devices. Wireless signal recognition
is becoming increasingly more significant for spectrum monitoring, spectrum
management, secure communications, among others. Consequently, comprehensive
spectrum awareness on the edge has the potential to serve as a key enabler for
the emerging beyond 5G networks. State-of-the-art studies in this domain have
(i) only focused on a single task - modulation or signal (protocol)
classification - which in many cases is insufficient information for a system
to act on, (ii) consider either radar or communication waveforms (homogeneous
waveform category), and (iii) does not address edge deployment during neural
network design phase. In this work, for the first time in the wireless
communication domain, we exploit the potential of deep neural networks based
multi-task learning (MTL) framework to simultaneously learn modulation and
signal classification tasks while considering heterogeneous wireless signals
such as radar and communication waveforms in the electromagnetic spectrum. The
proposed MTL architecture benefits from the mutual relation between the two
tasks in improving the classification accuracy as well as the learning
efficiency with a lightweight neural network model. We additionally include
experimental evaluations of the model with over-the-air collected samples and
demonstrate first-hand insight on model compression along with deep learning
pipeline for deployment on resource-constrained edge devices. We demonstrate
significant computational, memory, and accuracy improvement of the proposed
model over two reference architectures. In addition to modeling a lightweight
MTL model suitable for resource-constrained embedded radio platforms, we
provide a comprehensive heterogeneous wireless signals dataset for public use.
