Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. 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 (fifth generation) 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.

未来的通信网络必须解决稀缺的频谱，以适应异构无线设备的广泛增长。正在努力解决频谱共存、增强频谱意识和支持认证方案。无线信号识别在频谱监测、频谱管理、安全通信等方面变得越来越重要。因此，边缘的全面频谱感知有潜力成为超越 5G（第五代）网络的关键推动者。该领域最先进的研究 (i) 仅关注单一任务——调制或信号（协议）分类——在许多情况下，这对于系统采取行动而言信息不足，(ii) 考虑任一雷达或通信波形（同质波形类别），以及 (iii) 在神经网络设计阶段不解决边缘部署问题。在这项工作中，我们首次在无线通信领域利用基于深度神经网络的多任务学习 (MTL) 框架的潜力，在考虑雷达和通信波形等异构无线信号的同时，同时学习调制和信号分类任务在电磁频谱中。所提出的 MTL 架构受益于两个任务之间的相互关系，从而提高了分类精度以及轻量级神经网络模型的学习效率。我们还包括使用无线收集的样本对模型进行实验评估，并展示对模型压缩的第一手见解以及用于在资源受限的边缘设备上部署的深度学习管道。我们展示了所提出的模型在两个参考架构上的显着计算、内存和准确性改进。除了对适用于资源受限的嵌入式无线电平台的轻量级 MTL 模型进行建模外，我们还提供了一个全面的异构无线信号数据集供公众使用。